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ORCIDStefano Ermon
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- affiliation: Stanford University
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2020 – today
- 2025
[j13]Zhuo Zheng
, Stefano Ermon, Dongjun Kim, Liangpei Zhang, Yanfei Zhong:
Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model. IEEE Trans. Pattern Anal. Mach. Intell. 47(2): 725-741 (2025)- [c275]Silas Alberti, Kenan Hasanaliyev, Manav Shah, Stefano Ermon:
Data Unlearning in Diffusion Models. ICLR 2025 - [c274]Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce Chuyi Chen, Ines Dormoy, Jinyoung Kim, Samar Khanna, Zhuo Zheng, Stefano Ermon:
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data. ICLR 2025 - [c273]Joshua Kazdan, Hao Sun, Jiaqi Han, Felix Petersen, Frederick Vu, Stefano Ermon:
CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion. ICLR 2025 - [c272]Haowei Lin, Shanda Li, Haotian Ye, Yiming Yang, Stefano Ermon, Yitao Liang, Jianzhu Ma:
TFG-Flow: Training-free Guidance in Multimodal Generative Flow. ICLR 2025 - [c271]Juntong Shi, Minkai Xu, Harper Hua, Hengrui Zhang, Stefano Ermon, Jure Leskovec:
TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation. ICLR 2025 - [c270]Minkai Xu, Tomas Geffner, Karsten Kreis, Weili Nie, Yilun Xu, Jure Leskovec, Stefano Ermon, Arash Vahdat:
Energy-Based Diffusion Language Models for Text Generation. ICLR 2025 - [i272]Meihua Dang, Anikait Singh, Linqi Zhou, Stefano Ermon, Jiaming Song:
Personalized Preference Fine-tuning of Diffusion Models. CoRR abs/2501.06655 (2025) - [i271]Haowei Lin, Shanda Li, Haotian Ye, Yiming Yang, Stefano Ermon, Yitao Liang, Jianzhu Ma:
TFG-Flow: Training-free Guidance in Multimodal Generative Flow. CoRR abs/2501.14216 (2025) - [i270]Mingyu Kim, Dongjun Kim, Amman Yusuf, Stefano Ermon, Mi Jung Park:
Training-Free Safe Denoisers for Safe Use of Diffusion Models. CoRR abs/2502.08011 (2025) - [i269]Anikait Singh, Sheryl Hsu, Kyle Hsu, Eric Mitchell, Stefano Ermon, Tatsunori Hashimoto, Archit Sharma, Chelsea Finn:
FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users. CoRR abs/2502.19312 (2025) - [i268]Silas Alberti, Kenan Hasanaliyev, Manav Shah, Stefano Ermon:
Data Unlearning in Diffusion Models. CoRR abs/2503.01034 (2025) - [i267]Jerome Ku, Eric Nguyen, David W. Romero, Garyk Brixi, Brandon Yang, Anton Vorontsov, Ali Taghibakhshi, Amy X. Lu, Dave P. Burke, Greg Brockman, Stefano Massaroli, Christopher Ré, Patrick D. Hsu, Brian L. Hie, Stefano Ermon, Michael Poli:
Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale. CoRR abs/2503.01868 (2025) - [i266]Linqi Zhou, Stefano Ermon, Jiaming Song:
Inductive Moment Matching. CoRR abs/2503.07565 (2025) - [i265]Yashas Annadani, Syrine Belakaria, Stefano Ermon, Stefan Bauer, Barbara E. Engelhardt:
Preference-Guided Diffusion for Multi-Objective Offline Optimization. CoRR abs/2503.17299 (2025) - [i264]Syrine Belakaria, Joshua Kazdan, Charles Marx, Chris Cundy, Willie Neiswanger, Sanmi Koyejo, Barbara E. Engelhardt, Stefano Ermon:
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF. CoRR abs/2503.22137 (2025) - 2024
- [j12]Sara A. Miskovich, Willie Neiswanger, William Colocho, Claudio Emma, Jacqueline Garrahan, Timothy Maxwell, Christopher Mayes, Stefano Ermon, Auralee Edelen, Daniel Ratner:
Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives. Mach. Learn. Sci. Technol. 5(1): 15004 (2024) - [c269]Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec:
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. AAAI 2024: 320-328 - [c268]Jonathan Xu, Amna Elmustafa, Liya Weldegebriel, Emnet Negash, Richard Lee, Chenlin Meng, Stefano Ermon, David B. Lobell:
HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing. AAAI 2024: 22438-22446 - [c267]Ryan Park, Rafael Rafailov, Stefano Ermon, Chelsea Finn:
Disentangling Length from Quality in Direct Preference Optimization. ACL (Findings) 2024: 4998-5017 - [c266]Chris Cundy, Rishi Desai, Stefano Ermon:
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients. AISTATS 2024: 2809-2817 - [c265]Linqi Zhou, Andy Shih, Chenlin Meng, Stefano Ermon:
DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling. CVPR 2024: 4610-4619 - [c264]Jonathan Xu, Amna Elmustafa, Liya Weldegebriel, Emnet Negash, Richard Lee, Chenlin Meng, Stefano Ermon, David B. Lobell:
HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing. CVPR Workshops 2024: 5366-5374 - [c263]Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, Nikhil Naik:
Diffusion Model Alignment Using Direct Preference Optimization. CVPR 2024: 8228-8238 - [c262]Shu Zhang, Xinyi Yang, Yihao Feng, Can Qin, Chia-Chih Chen, Ning Yu, Zeyuan Chen, Huan Wang, Silvio Savarese, Stefano Ermon, Caiming Xiong, Ran Xu:
HIVE: Harnessing Human Feedback for Instructional Visual Editing. CVPR 2024: 9026-9036 - [c261]Hao Li, Yang Zou, Ying Wang, Orchid Majumder, Yusheng Xie, R. Manmatha, Ashwin Swaminathan, Zhuowen Tu, Stefano Ermon, Stefano Soatto:
On the Scalability of Diffusion-based Text-to-Image Generation. CVPR 2024: 9400-9409 - [c260]Qian Cao, Nemin Wu, Zhangyu Wang, Zeping Liu, Yanlin Qi, Jielu Zhang, Joshua Ni, Xiaobai Angela Yao, Hongxu Ma, Lan Mu, Stefano Ermon, Tanuja Ganu, Akshay Nambi, Ni Lao, Gengchen Mai:
TorchSpatial: A Python Package for Spatial Representation Learning and Geo-Aware Model Development. GeoIndstry@SIGSPATIAL 2024: 39-42 - [c259]Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, Bin Cui:
Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing. ICLR 2024 - [c258]Chris Cundy, Stefano Ermon:
SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking. ICLR 2024 - [c257]Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J. Zico Kolter, Ruslan Salakhutdinov, Stefano Ermon:
Manifold Preserving Guided Diffusion. ICLR 2024 - [c256]Samar Khanna, Patrick Liu, Linqi Zhou, Chenlin Meng, Robin Rombach, Marshall Burke, David B. Lobell, Stefano Ermon:
DiffusionSat: A Generative Foundation Model for Satellite Imagery. ICLR 2024 - [c255]Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, Stefano Ermon:
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion. ICLR 2024 - [c254]Rohin Manvi, Samar Khanna, Gengchen Mai, Marshall Burke, David B. Lobell, Stefano Ermon:
GeoLLM: Extracting Geospatial Knowledge from Large Language Models. ICLR 2024 - [c253]Charlotte Nicks, Eric Mitchell, Rafael Rafailov, Archit Sharma, Christopher D. Manning, Chelsea Finn, Stefano Ermon:
Language Model Detectors Are Easily Optimized Against. ICLR 2024 - [c252]Linqi Zhou, Aaron Lou, Samar Khanna, Stefano Ermon:
Denoising Diffusion Bridge Models. ICLR 2024 - [c251]Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, Bin Cui:
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs. ICML 2024 - [c250]Aaron Lou, Chenlin Meng, Stefano Ermon:
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution. ICML 2024 - [c249]Rohin Manvi, Samar Khanna, Marshall Burke, David B. Lobell, Stefano Ermon:
Large Language Models are Geographically Biased. ICML 2024 - [c248]Rom N. Parnichkun, Stefano Massaroli, Alessandro Moro, Jimmy T. H. Smith, Ramin M. Hasani, Mathias Lechner, Qi An, Christopher Ré, Hajime Asama, Stefano Ermon, Taiji Suzuki, Michael Poli, Atsushi Yamashita:
State-Free Inference of State-Space Models: The *Transfer Function* Approach. ICML 2024 - [c247]Michael Poli, Armin W. Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian L. Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli:
Mechanistic Design and Scaling of Hybrid Architectures. ICML 2024 - [c246]Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar:
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. ICML 2024 - [c245]Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar:
Equivariant Graph Neural Operator for Modeling 3D Dynamics. ICML 2024 - [c244]Syrine Belakaria, Ben Letham, Jana Doppa, Barbara Engelhardt, Stefano Ermon, Eytan Bakshy:
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes. NeurIPS 2024 - [c243]Siyi Gu, Minkai Xu, Alexander S. Powers, Weili Nie, Tomas Geffner, Karsten Kreis, Jure Leskovec, Arash Vahdat, Stefano Ermon:
Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization. NeurIPS 2024 - [c242]Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon:
Geometric Trajectory Diffusion Models. NeurIPS 2024 - [c241]Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon:
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher. NeurIPS 2024 - [c240]Gabriel Nobis, Maximilian Springenberg, Marco Aversa, Michael Detzel, Rembert Daems, Roderick Murray-Smith, Shinichi Nakajima, Sebastian Lapuschkin, Stefano Ermon, Tolga Birdal, Manfred Opper, Christoph Knochenhauer, Luis Oala, Wojciech Samek:
Generative Fractional Diffusion Models. NeurIPS 2024 - [c239]Felix Petersen, Christian Borgelt, Stefano Ermon:
TrAct: Making First-layer Pre-Activations Trainable. NeurIPS 2024 - [c238]Felix Petersen, Christian Borgelt, Tobias Sutter, Hilde Kuehne, Oliver Deussen, Stefano Ermon:
Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms. NeurIPS 2024 - [c237]Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel, Stefano Ermon:
Convolutional Differentiable Logic Gate Networks. NeurIPS 2024 - [c236]Nikil Roashan Selvam, Amil Merchant, Stefano Ermon:
Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations. NeurIPS 2024 - [c235]Nemin Wu, Qian Cao, Zhangyu Wang, Zeping Liu, Yanlin Qi, Jielu Zhang, Joshua Ni, Xiaobai Angela Yao, Hongxu Ma, Lan Mu, Stefano Ermon, Tanuja Ganu, Akshay Nambi, Ni Lao, Gengchen Mai:
TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning. NeurIPS 2024 - [c234]Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Y. Zou, Stefano Ermon:
TFG: Unified Training-Free Guidance for Diffusion Models. NeurIPS 2024 - [c233]Zhuo Zheng, Yanfei Zhong, Liangpei Zhang, Stefano Ermon:
Segment Any Change. NeurIPS 2024 - [c232]Zhengbang Zhu, Minghuan Liu, Liyuan Mao, Bingyi Kang, Minkai Xu, Yong Yu, Stefano Ermon, Weinan Zhang:
MADiff: Offline Multi-agent Learning with Diffusion Models. NeurIPS 2024 - [i263]Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar:
Equivariant Graph Neural Operator for Modeling 3D Dynamics. CoRR abs/2401.11037 (2024) - [i262]Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, Bin Cui:
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs. CoRR abs/2401.11708 (2024) - [i261]Zhuo Zheng, Yanfei Zhong, Liangpei Zhang, Stefano Ermon:
Segment Any Change. CoRR abs/2402.01188 (2024) - [i260]Rohin Manvi, Samar Khanna, Marshall Burke, David B. Lobell, Stefano Ermon:
Large Language Models are Geographically Biased. CoRR abs/2402.02680 (2024) - [i259]Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec:
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. CoRR abs/2402.08383 (2024) - [i258]Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, Bin Cui:
Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing. CoRR abs/2402.16627 (2024) - [i257]Michael Poli, Armin W. Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian L. Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli:
Mechanistic Design and Scaling of Hybrid Architectures. CoRR abs/2403.17844 (2024) - [i256]Ryan Park, Rafael Rafailov, Stefano Ermon, Chelsea Finn:
Disentangling Length from Quality in Direct Preference Optimization. CoRR abs/2403.19159 (2024) - [i255]Hao Li, Yang Zou, Ying Wang, Orchid Majumder, Yusheng Xie, R. Manmatha, Ashwin Swaminathan, Zhuowen Tu, Stefano Ermon, Stefano Soatto:
On the Scalability of Diffusion-based Text-to-Image Generation. CoRR abs/2404.02883 (2024) - [i254]Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar:
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. CoRR abs/2404.14367 (2024) - [i253]Rom N. Parnichkun, Stefano Massaroli, Alessandro Moro, Jimmy T. H. Smith, Ramin M. Hasani, Mathias Lechner, Qi An, Christopher Ré, Hajime Asama, Stefano Ermon, Taiji Suzuki, Atsushi Yamashita, Michael Poli:
State-Free Inference of State-Space Models: The Transfer Function Approach. CoRR abs/2405.06147 (2024) - [i252]Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon:
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher. CoRR abs/2405.14822 (2024) - [i251]Samar Khanna, Medhanie Irgau, David B. Lobell, Stefano Ermon:
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts. CoRR abs/2406.10973 (2024) - [i250]Nemin Wu, Qian Cao, Zhangyu Wang, Zeping Liu, Yanlin Qi, Jielu Zhang, Joshua Ni, Xiaobai Angela Yao, Hongxu Ma, Lan Mu, Stefano Ermon, Tanuja Ganu, Akshay Nambi, Ni Lao, Gengchen Mai:
TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning. CoRR abs/2406.15658 (2024) - [i249]Zhuo Zheng, Stefano Ermon, Dongjun Kim, Liangpei Zhang, Yanfei Zhong:
Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model. CoRR abs/2406.17998 (2024) - [i248]Siyi Gu, Minkai Xu, Alexander S. Powers, Weili Nie, Tomas Geffner, Karsten Kreis, Jure Leskovec, Arash Vahdat, Stefano Ermon:
Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization. CoRR abs/2407.01648 (2024) - [i247]Ling Yang, Zixiang Zhang, Zhilong Zhang, Xingchao Liu, Minkai Xu, Wentao Zhang, Chenlin Meng, Stefano Ermon, Bin Cui:
Consistency Flow Matching: Defining Straight Flows with Velocity Consistency. CoRR abs/2407.02398 (2024) - [i246]Eunwoo Kim, Un Yang, Cheol Lae Roh, Stefano Ermon:
Unsupervised Anomaly Detection Using Diffusion Trend Analysis. CoRR abs/2407.09578 (2024) - [i245]Syrine Belakaria, Benjamin Letham, Janardhan Rao Doppa, Barbara Engelhardt, Stefano Ermon, Eytan Bakshy:
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes. CoRR abs/2407.09739 (2024) - [i244]Joshua Kazdan, Hao Sun, Jiaqi Han, Felix Petersen, Stefano Ermon:
CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion. CoRR abs/2409.07025 (2024) - [i243]Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Zou, Stefano Ermon:
TFG: Unified Training-Free Guidance for Diffusion Models. CoRR abs/2409.15761 (2024) - [i242]Charles Marx, Volodymyr Kuleshov, Stefano Ermon:
Calibrated Probabilistic Forecasts for Arbitrary Sequences. CoRR abs/2409.19157 (2024) - [i241]Yangming Li, Chieh-Hsin Lai, Carola-Bibiane Schönlieb, Yuki Mitsufuji, Stefano Ermon:
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space. CoRR abs/2410.01796 (2024) - [i240]Rohin Manvi, Anikait Singh, Stefano Ermon:
Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation. CoRR abs/2410.02725 (2024) - [i239]Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce Chuyi Chen, Ines Dormoy, Jinyoung Kim, Samar Khanna, Zhuo Zheng, Stefano Ermon:
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data. CoRR abs/2410.06234 (2024) - [i238]Bohan Zeng, Ling Yang, Siyu Li, Jiaming Liu, Zixiang Zhang, Juanxi Tian, Kaixin Zhu, Yongzhen Guo, Fu-Yun Wang, Minkai Xu, Stefano Ermon, Wentao Zhang:
Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis. CoRR abs/2410.07155 (2024) - [i237]Felix Petersen, Christian Borgelt, Aashwin Ananda Mishra, Stefano Ermon:
Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation. CoRR abs/2410.08125 (2024) - [i236]Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon:
Geometric Trajectory Diffusion Models. CoRR abs/2410.13027 (2024) - [i235]Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Bac Nguyen, Stefano Ermon, Yuki Mitsufuji:
G2D2: Gradient-guided Discrete Diffusion for image inverse problem solving. CoRR abs/2410.14710 (2024) - [i234]Bac Nguyen, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Stefano Ermon, Yuki Mitsufuji:
Mitigating Embedding Collapse in Diffusion Models for Categorical Data. CoRR abs/2410.14758 (2024) - [i233]Felix Petersen, Christian Borgelt, Tobias Sutter, Hilde Kuehne, Oliver Deussen, Stefano Ermon:
Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms. CoRR abs/2410.19055 (2024) - [i232]Juntong Shi, Minkai Xu, Harper Hua, Hengrui Zhang, Stefano Ermon, Jure Leskovec:
TabDiff: a Multi-Modal Diffusion Model for Tabular Data Generation. CoRR abs/2410.20626 (2024) - [i231]Minkai Xu, Tomas Geffner, Karsten Kreis, Weili Nie, Yilun Xu, Jure Leskovec, Stefano Ermon, Arash Vahdat:
Energy-Based Diffusion Language Models for Text Generation. CoRR abs/2410.21357 (2024) - [i230]Jiaqi Han, Mingjian Jiang, Yuxuan Song, Jure Leskovec, Stefano Ermon, Minkai Xu:
f-PO: Generalizing Preference Optimization with f-divergence Minimization. CoRR abs/2410.21662 (2024) - [i229]Felix Petersen, Christian Borgelt, Stefano Ermon:
TrAct: Making First-layer Pre-Activations Trainable. CoRR abs/2410.23970 (2024) - [i228]Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel, Stefano Ermon:
Convolutional Differentiable Logic Gate Networks. CoRR abs/2411.04732 (2024) - [i227]Syrine Belakaria, Alaleh Ahmadianshalchi, Barbara Engelhardt, Stefano Ermon, Janardhan Rao Doppa:
Non-Myopic Multi-Objective Bayesian Optimization. CoRR abs/2412.08085 (2024) - [i226]Nikil Roashan Selvam, Amil Merchant, Stefano Ermon:
Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations. CoRR abs/2412.08292 (2024) - [i225]Hao Li, Shamit Lal, Zhiheng Li, Yusheng Xie, Ying Wang, Yang Zou, Orchid Majumder, R. Manmatha, Zhuowen Tu, Stefano Ermon, Stefano Soatto, Ashwin Swaminathan:
Efficient Scaling of Diffusion Transformers for Text-to-Image Generation. CoRR abs/2412.12391 (2024) - 2023
- [j11]Gengchen Mai, Chiyu Jiang, Weiwei Sun, Rui Zhu, Yao Xuan, Ling Cai, Krzysztof Janowicz, Stefano Ermon, Ni Lao:
Towards general-purpose representation learning of polygonal geometries. GeoInformatica 27(2): 289-340 (2023) - [j10]Muyang Li, Ji Lin, Chenlin Meng, Stefano Ermon, Song Han, Jun-Yan Zhu:
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models. IEEE Trans. Pattern Anal. Mach. Intell. 45(12): 14465-14480 (2023) - [j9]Berivan Isik, Kristy Choi, Xin Zheng, Tsachy Weissman, Stefano Ermon, H.-S. Philip Wong, Armin Alaghi:
Neural Network Compression for Noisy Storage Devices. ACM Trans. Embed. Comput. Syst. 22(3): 58:1-58:29 (2023) - [j8]Arundhati Banerjee, Soham R. Phade, Stefano Ermon, Stephan Zheng:
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning. Trans. Mach. Learn. Res. 2023 (2023) - [j7]Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew K. Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakas, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartlomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, Cèsar Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodolà, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, Ethan J. Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, François Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-López, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Shevlin, Hinrich Schütze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Kocon, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse H. Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jörg Frohberg, Jos Rozen, José Hernández-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, Kory W. Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Colón, Luke Metz, Lütfi Kerem Senel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, María José Ramírez-Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Mátyás Schubert, Medina Baitemirova, Melody Arnaud, Melvin McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael I. Ivanitskiy, Michael Starritt, Michael Strube, Michal Swedrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T., Nanyun Peng, Nathan A. Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Milkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan LeBras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima (Shammie) Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven T. Piantadosi, Stuart M. Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsu Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay V. Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, Zirui Wang, Ziyi Wu:
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. Trans. Mach. Learn. Res. 2023 (2023) - [c231]Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon:
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. AAAI 2023: 11016-11024 - [c230]Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan:
But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI. AISTATS 2023: 7375-7391 - [c229]Michael Poli, Stefano Massaroli, Stefano Ermon, Bryan Wilder, Eric Horvitz:
Ideal Abstractions for Decision-Focused Learning. AISTATS 2023: 10223-10234 - [c228]Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans:
On Distillation of Guided Diffusion Models. CVPR 2023: 14297-14306 - [c227]Chenwei Wu, Li Erran Li, Stefano Ermon, Patrick Haffner, Rong Ge, Zaiwei Zhang:
The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models. ICBINB 2023: 118-126 - [c226]Bram Wallace, Akash Gokul, Stefano Ermon, Nikhil Naik:
End-to-End Diffusion Latent Optimization Improves Classifier Guidance. ICCV 2023: 7246-7256 - [c225]Can Qin, Ning Yu, Chen Xing, Shu Zhang, Zeyuan Chen, Stefano Ermon, Yun Fu, Caiming Xiong, Ran Xu:
GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation. ICCV 2023: 23028-23039 - [c224]Benedikt Boecking, Nicholas Carl Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski:
Generative Modeling Helps Weak Supervision (and Vice Versa). ICLR 2023 - [c223]Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon:
Extreme Q-Learning: MaxEnt RL without Entropy. ICLR 2023 - [c222]Kuno Kim, Stefano Ermon:
Understanding and Adopting Rational Behavior by Bellman Score Estimation. ICLR 2023 - [c221]Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon:
Dual Diffusion Implicit Bridges for Image-to-Image Translation. ICLR 2023 - [c220]Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon:
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation. ICML 2023: 18365-18398 - [c219]Aaron Lou, Stefano Ermon:
Reflected Diffusion Models. ICML 2023: 22675-22701 - [c218]Gengchen Mai, Ni Lao, Yutong He, Jiaming Song, Stefano Ermon:
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations. ICML 2023: 23498-23515 - [c217]Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon:
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration. ICML 2023: 25501-25522 - [c216]Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y. Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, Christopher Ré:
Hyena Hierarchy: Towards Larger Convolutional Language Models. ICML 2023: 28043-28078 - [c215]Andy Shih, Dorsa Sadigh, Stefano Ermon:
Long Horizon Temperature Scaling. ICML 2023: 31422-31434 - [c214]Minkai Xu, Alexander S. Powers, Ron O. Dror, Stefano Ermon, Jure Leskovec:
Geometric Latent Diffusion Models for 3D Molecule Generation. ICML 2023: 38592-38610 - [c213]Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, Stefano Ermon:
Deep Latent State Space Models for Time-Series Generation. ICML 2023: 42625-42643 - [c212]Minkai Xu, Meng Liu, Wengong Jin, Shuiwang Ji, Jure Leskovec, Stefano Ermon:
Graph and Geometry Generative Modeling for Drug Discovery. KDD 2023: 5833-5834 - [c211]Chenqing Hua, Sitao Luan, Minkai Xu, Zhitao Ying, Jie Fu, Stefano Ermon, Doina Precup:
MUDiff: Unified Diffusion for Complete Molecule Generation. LoG 2023: 33 - [c210]Alexandre Lacoste, Nils Lehmann, Pau Rodríguez, Evan D. Sherwin, Hannah Kerner, Björn Lütjens, Jeremy Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, Mehmet Gunturkun, Gabriel Huang, David Vázquez, Dava Newman, Yoshua Bengio, Stefano Ermon, Xiaoxiang Zhu:
GEO-Bench: Toward Foundation Models for Earth Monitoring. NeurIPS 2023 - [c209]Tony Lee, Michihiro Yasunaga, Chenlin Meng, Yifan Mai, Joon Sung Park, Agrim Gupta, Yunzhi Zhang, Deepak Narayanan, Hannah Teufel, Marco Bellagente, Minguk Kang, Taesung Park, Jure Leskovec, Jun-Yan Zhu, Fei-Fei Li, Jiajun Wu, Stefano Ermon, Percy Liang:
Holistic Evaluation of Text-to-Image Models. NeurIPS 2023 - [c208]Aaron Lou, Minkai Xu, Adam Farris, Stefano Ermon:
Scaling Riemannian Diffusion Models. NeurIPS 2023 - [c207]Charlie Marx, Sofian Zalouk, Stefano Ermon:
Calibration by Distribution Matching: Trainable Kernel Calibration Metrics. NeurIPS 2023 - [c206]Stefano Massaroli, Michael Poli, Daniel Y. Fu, Hermann Kumbong, Rom N. Parnichkun, David W. Romero, Aman Timalsina, Quinn McIntyre, Beidi Chen, Atri Rudra, Ce Zhang, Christopher Ré, Stefano Ermon, Yoshua Bengio:
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions. NeurIPS 2023 - [c205]Eric Nguyen, Michael Poli, Marjan Faizi, Armin W. Thomas, Michael Wornow, Callum Birch-Sykes, Stefano Massaroli, Aman Patel, Clayton M. Rabideau, Yoshua Bengio, Stefano Ermon, Christopher Ré, Stephen Baccus:
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution. NeurIPS 2023 - [c204]Can Qin, Shu Zhang, Ning Yu, Yihao Feng, Xinyi Yang, Yingbo Zhou, Huan Wang, Juan Carlos Niebles, Caiming Xiong, Silvio Savarese, Stefano Ermon, Yun Fu, Ran Xu:
UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild. NeurIPS 2023 - [c203]Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D. Manning, Stefano Ermon, Chelsea Finn:
Direct Preference Optimization: Your Language Model is Secretly a Reward Model. NeurIPS 2023 - [c202]Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari:
Parallel Sampling of Diffusion Models. NeurIPS 2023 - [c201]Yuxuan Song, Jingjing Gong, Minkai Xu, Ziyao Cao, Yanyan Lan, Stefano Ermon, Hao Zhou, Wei-Ying Ma:
Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation. NeurIPS 2023 - [c200]Chenlin Meng, Jiaming Song, Shuang Li, Jun-Yan Zhu, Stefano Ermon, Tsung-Yi Lin, Chen-Hsuan Lin, Karsten Kreis:
SIGGRAPH 2023 Course on Diffusion Models. SIGGRAPH Courses 2023: 7:1-7:113 - [i224]Enci Liu, Chenlin Meng, Matthew Kolodner, Eun Jee Sung, Sihang Chen, Marshall Burke, David B. Lobell, Stefano Ermon:
Building Coverage Estimation with Low-resolution Remote Sensing Imagery. CoRR abs/2301.01449 (2023) - [i223]Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon:
Extreme Q-Learning: MaxEnt RL without Entropy. CoRR abs/2301.02328 (2023) - [i222]Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon:
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration. CoRR abs/2301.12686 (2023) - [i221]Andy Shih, Dorsa Sadigh, Stefano Ermon:
Long Horizon Temperature Scaling. CoRR abs/2302.03686 (2023) - [i220]Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y. Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, Christopher Ré:
Hyena Hierarchy: Towards Larger Convolutional Language Models. CoRR abs/2302.10866 (2023) - [i219]Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon:
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. CoRR abs/2303.02569 (2023) - [i218]Shu Zhang, Xinyi Yang, Yihao Feng, Can Qin, Chia-Chih Chen, Ning Yu, Zeyuan Chen, Huan Wang, Silvio Savarese, Stefano Ermon, Caiming Xiong, Ran Xu:
HIVE: Harnessing Human Feedback for Instructional Visual Editing. CoRR abs/2303.09618 (2023) - [i217]Can Qin, Ning Yu, Chen Xing, Shu Zhang, Zeyuan Chen, Stefano Ermon, Yun Fu, Caiming Xiong, Ran Xu:
GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation. CoRR abs/2303.10056 (2023) - [i216]Bram Wallace, Akash Gokul, Stefano Ermon, Nikhil Naik:
End-to-End Diffusion Latent Optimization Improves Classifier Guidance. CoRR abs/2303.13703 (2023) - [i215]Michael Poli, Stefano Massaroli, Stefano Ermon, Bryan Wilder, Eric Horvitz:
Ideal Abstractions for Decision-Focused Learning. CoRR abs/2303.17062 (2023) - [i214]Arundhati Banerjee, Soham Phade, Stefano Ermon, Stephan Zheng:
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning. CoRR abs/2304.04668 (2023) - [i213]Aaron Lou, Stefano Ermon:
Reflected Diffusion Models. CoRR abs/2304.04740 (2023) - [i212]Chenqing Hua, Sitao Luan, Minkai Xu, Rex Ying, Jie Fu, Stefano Ermon, Doina Precup:
MUDiff: Unified Diffusion for Complete Molecule Generation. CoRR abs/2304.14621 (2023) - [i211]Gengchen Mai, Ni Lao, Yutong He, Jiaming Song, Stefano Ermon:
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations. CoRR abs/2305.01118 (2023) - [i210]Minkai Xu, Alexander S. Powers, Ron O. Dror, Stefano Ermon, Jure Leskovec:
Geometric Latent Diffusion Models for 3D Molecule Generation. CoRR abs/2305.01140 (2023) - [i209]Can Qin, Shu Zhang, Ning Yu, Yihao Feng, Xinyi Yang, Yingbo Zhou, Huan Wang, Juan Carlos Niebles, Caiming Xiong, Silvio Savarese, Stefano Ermon, Yun Fu, Ran Xu:
UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild. CoRR abs/2305.11147 (2023) - [i208]Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari:
Parallel Sampling of Diffusion Models. CoRR abs/2305.16317 (2023) - [i207]Zhengbang Zhu, Minghuan Liu, Liyuan Mao, Bingyi Kang, Minkai Xu, Yong Yu, Stefano Ermon, Weinan Zhang:
MADiff: Offline Multi-agent Learning with Diffusion Models. CoRR abs/2305.17330 (2023) - [i206]Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn:
Direct Preference Optimization: Your Language Model is Secretly a Reward Model. CoRR abs/2305.18290 (2023) - [i205]Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji, Stefano Ermon:
On the Equivalence of Consistency-Type Models: Consistency Models, Consistent Diffusion Models, and Fokker-Planck Regularization. CoRR abs/2306.00367 (2023) - [i204]Alexandre Lacoste, Nils Lehmann, Pau Rodríguez, Evan David Sherwin, Hannah Kerner, Björn Lütjens, Jeremy Andrew Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, Mehmet Gunturkun, Gabriel Huang, David Vázquez, Dava Newman, Yoshua Bengio, Stefano Ermon, Xiao Xiang Zhu:
GEO-Bench: Toward Foundation Models for Earth Monitoring. CoRR abs/2306.03831 (2023) - [i203]Chris Cundy, Stefano Ermon:
SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking. CoRR abs/2306.05426 (2023) - [i202]Eric Nguyen, Michael Poli, Marjan Faizi, Armin W. Thomas, Callum Birch-Sykes, Michael Wornow, Aman Patel, Clayton M. Rabideau, Stefano Massaroli, Yoshua Bengio, Stefano Ermon, Stephen A. Baccus, Christopher Ré:
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution. CoRR abs/2306.15794 (2023) - [i201]Gengchen Mai, Yao Xuan, Wenyun Zuo, Yutong He, Jiaming Song, Stefano Ermon, Krzysztof Janowicz, Ni Lao:
Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions. CoRR abs/2306.17624 (2023) - [i200]Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR abs/2307.08423 (2023) - [i199]Jonathan Xu, Amna Elmustafa, Liya Weldegebriel, Emnet Negash, Richard Lee, Chenlin Meng, Stefano Ermon, David B. Lobell:
HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing. CoRR abs/2308.12061 (2023) - [i198]Linqi Zhou, Aaron Lou, Samar Khanna, Stefano Ermon:
Denoising Diffusion Bridge Models. CoRR abs/2309.16948 (2023) - [i197]Gengchen Mai, Ni Lao, Weiwei Sun, Yuchi Ma, Jiaming Song, Chenlin Meng, Hongxu Ma, Jinmeng Rao, Ziyuan Li, Stefano Ermon:
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution. CoRR abs/2310.00413 (2023) - [i196]Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, Stefano Ermon:
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion. CoRR abs/2310.02279 (2023) - [i195]Chenwei Wu, Li Erran Li, Stefano Ermon, Patrick Haffner, Rong Ge, Zaiwei Zhang:
The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models. CoRR abs/2310.02777 (2023) - [i194]Rohin Manvi, Samar Khanna, Gengchen Mai, Marshall Burke, David B. Lobell, Stefano Ermon:
GeoLLM: Extracting Geospatial Knowledge from Large Language Models. CoRR abs/2310.06213 (2023) - [i193]Aaron Lou, Chenlin Meng, Stefano Ermon:
Discrete Diffusion Language Modeling by Estimating the Ratios of the Data Distribution. CoRR abs/2310.16834 (2023) - [i192]Gabriel Nobis, Marco Aversa, Maximilian Springenberg, Michael Detzel, Stefano Ermon, Shinichi Nakajima, Roderick Murray-Smith, Sebastian Lapuschkin, Christoph Knochenhauer, Luis Oala, Wojciech Samek:
Generative Fractional Diffusion Models. CoRR abs/2310.17638 (2023) - [i191]Stefano Massaroli, Michael Poli, Daniel Y. Fu, Hermann Kumbong, Rom N. Parnichkun, Aman Timalsina, David W. Romero, Quinn McIntyre, Beidi Chen, Atri Rudra, Ce Zhang, Christopher Ré, Stefano Ermon, Yoshua Bengio:
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions. CoRR abs/2310.18780 (2023) - [i190]Aaron Lou, Minkai Xu, Stefano Ermon:
Scaling Riemannian Diffusion Models. CoRR abs/2310.20030 (2023) - [i189]Charles Marx, Sofian Zalouk, Stefano Ermon:
Calibration by Distribution Matching: Trainable Kernel Calibration Metrics. CoRR abs/2310.20211 (2023) - [i188]Tony Lee, Michihiro Yasunaga, Chenlin Meng, Yifan Mai, Joon Sung Park, Agrim Gupta, Yunzhi Zhang, Deepak Narayanan, Hannah Benita Teufel, Marco Bellagente, Minguk Kang, Taesung Park, Jure Leskovec, Jun-Yan Zhu, Li Fei-Fei, Jiajun Wu, Stefano Ermon, Percy Liang:
Holistic Evaluation of Text-To-Image Models. CoRR abs/2311.04287 (2023) - [i187]Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, Nikhil Naik:
Diffusion Model Alignment Using Direct Preference Optimization. CoRR abs/2311.12908 (2023) - [i186]Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J. Zico Kolter, Ruslan Salakhutdinov, Stefano Ermon:
Manifold Preserving Guided Diffusion. CoRR abs/2311.16424 (2023) - [i185]Linqi Zhou, Andy Shih, Chenlin Meng, Stefano Ermon:
DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling. CoRR abs/2311.17082 (2023) - [i184]Samar Khanna, Patrick Liu, Linqi Zhou, Chenlin Meng, Robin Rombach, Marshall Burke, David B. Lobell, Stefano Ermon:
DiffusionSat: A Generative Foundation Model for Satellite Imagery. CoRR abs/2312.03606 (2023) - [i183]Yuxuan Song, Jingjing Gong, Minkai Xu, Ziyao Cao, Yanyan Lan, Stefano Ermon, Hao Zhou, Wei-Ying Ma:
Equivariant Flow Matching with Hybrid Probability Transport. CoRR abs/2312.07168 (2023) - 2022
- [c199]Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David B. Lobell, Stefano Ermon:
IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling. AAAI 2022: 12034-12042 - [c198]Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon:
Density Ratio Estimation via Infinitesimal Classification. AISTATS 2022: 2552-2573 - [c197]Shuvam Chakraborty, Burak Uzkent, Kumar Ayush, Kumar Tanmay, Evan Sheehan, Stefano Ermon:
Efficient Conditional Pre-training for Transfer Learning. CVPR Workshops 2022: 4240-4249 - [c196]Amna Elmustafa, Erik Rozi, Yutong He, Gengchen Mai, Stefano Ermon, Marshall Burke, David B. Lobell:
Understanding economic development in rural Africa using satellite imagery, building footprints and deep models. SIGSPATIAL/GIS 2022: 89:1-89:4 - [c195]Gengchen Mai, Chris Cundy, Kristy Choi, Yingjie Hu, Ni Lao, Stefano Ermon:
Towards a foundation model for geospatial artificial intelligence (vision paper). SIGSPATIAL/GIS 2022: 106:1-106:4 - [c194]Andy Shih, Stefano Ermon, Dorsa Sadigh:
Conditional Imitation Learning for Multi-Agent Games. HRI 2022: 166-175 - [c193]Yang Song, Liyue Shen, Lei Xing, Stefano Ermon:
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models. ICLR 2022 - [c192]Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger:
An Experimental Design Perspective on Model-Based Reinforcement Learning. ICLR 2022 - [c191]Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon:
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. ICLR 2022 - [c190]Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang:
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation. ICLR 2022 - [c189]Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon:
Comparing Distributions by Measuring Differences that Affect Decision Making. ICLR 2022 - [c188]Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani:
Imitation Learning by Estimating Expertise of Demonstrators. ICML 2022: 1732-1748 - [c187]Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon:
Modular Conformal Calibration. ICML 2022: 15180-15195 - [c186]Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon:
ButterflyFlow: Building Invertible Layers with Butterfly Matrices. ICML 2022: 15360-15375 - [c185]Rui Shu, Stefano Ermon:
Bit Prioritization in Variational Autoencoders via Progressive Coding. ICML 2022: 20141-20155 - [c184]Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon:
A General Recipe for Likelihood-free Bayesian Optimization. ICML 2022: 20384-20404 - [c183]Samarth Sinha, Jiaming Song, Animesh Garg, Stefano Ermon:
Experience Replay with Likelihood-free Importance Weights. L4DC 2022: 110-123 - [c182]Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, Stefano Ermon:
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery. NeurIPS 2022 - [c181]Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré:
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness. NeurIPS 2022 - [c180]Yann Dubois, Stefano Ermon, Tatsunori B. Hashimoto, Percy Liang:
Improving Self-Supervised Learning by Characterizing Idealized Representations. NeurIPS 2022 - [c179]Divyansh Garg, Skanda Vaidyanath, Kuno Kim, Jiaming Song, Stefano Ermon:
LISA: Learning Interpretable Skill Abstractions from Language. NeurIPS 2022 - [c178]Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song:
Denoising Diffusion Restoration Models. NeurIPS 2022 - [c177]Muyang Li, Ji Lin, Chenlin Meng, Stefano Ermon, Song Han, Jun-Yan Zhu:
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models. NeurIPS 2022 - [c176]Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger:
Exploration via Planning for Information about the Optimal Trajectory. NeurIPS 2022 - [c175]Chenlin Meng, Kristy Choi, Jiaming Song, Stefano Ermon:
Concrete Score Matching: Generalized Score Matching for Discrete Data. NeurIPS 2022 - [c174]Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon:
Generalizing Bayesian Optimization with Decision-theoretic Entropies. NeurIPS 2022 - [c173]Michael Poli, Stefano Massaroli, Federico Berto, Jinkyoo Park, Tri Dao, Christopher Ré, Stefano Ermon:
Transform Once: Efficient Operator Learning in Frequency Domain. NeurIPS 2022 - [c172]Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon:
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations. NeurIPS 2022 - [c171]Andy Shih, Dorsa Sadigh, Stefano Ermon:
Training and Inference on Any-Order Autoregressive Models the Right Way. NeurIPS 2022 - [c170]Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone:
Local calibration: metrics and recalibration. UAI 2022: 1286-1295 - [i182]Andy Shih, Stefano Ermon, Dorsa Sadigh:
Conditional Imitation Learning for Multi-Agent Games. CoRR abs/2201.01448 (2022) - [i181]Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song:
Denoising Diffusion Restoration Models. CoRR abs/2201.11793 (2022) - [i180]Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani:
Imitation Learning by Estimating Expertise of Demonstrators. CoRR abs/2202.01288 (2022) - [i179]Divyansh Garg, Skanda Vaidyanath, Kuno Kim, Jiaming Song, Stefano Ermon:
LISA: Learning Interpretable Skill Abstractions from Language. CoRR abs/2203.00054 (2022) - [i178]Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang:
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation. CoRR abs/2203.02923 (2022) - [i177]Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon:
Dual Diffusion Implicit Bridges for Image-to-Image Translation. CoRR abs/2203.08382 (2022) - [i176]Benedikt Boecking, Willie Neiswanger, Nicholas Carl Roberts, Stefano Ermon, Frederic Sala, Artur Dubrawski:
Generative Modeling Helps Weak Supervision (and Vice Versa). CoRR abs/2203.12023 (2022) - [i175]Yutong He, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon:
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution. CoRR abs/2204.01736 (2022) - [i174]Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon:
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations. CoRR abs/2204.07673 (2022) - [i173]Andy Shih, Dorsa Sadigh, Stefano Ermon:
Training and Inference on Any-Order Autoregressive Models the Right Way. CoRR abs/2205.13554 (2022) - [i172]Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré:
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness. CoRR abs/2205.14135 (2022) - [i171]Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew K. Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakas, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartlomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, Cèsar Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodolà, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, Ethan J. Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, François Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-López, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Shevlin, Hinrich Schütze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Kocon, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse H. Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jörg Frohberg, Jos Rozen, José Hernández-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, Kory W. Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Colón, Luke Metz, Lütfi Kerem Senel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, María José Ramírez-Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Mátyás Schubert, Medina Baitemirova, Melody Arnaud, Melvin McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael I. Ivanitskiy, Michael Starritt, Michael Strube, Michal Swedrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T., Nanyun Peng, Nathan A. Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Milkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan LeBras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima (Shammie) Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven T. Piantadosi, Stuart M. Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsu Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay V. Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, Zirui Wang, Ziyi Wu:
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. CoRR abs/2206.04615 (2022) - [i170]Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon:
Modular Conformal Calibration. CoRR abs/2206.11468 (2022) - [i169]Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon:
A General Recipe for Likelihood-free Bayesian Optimization. CoRR abs/2206.13035 (2022) - [i168]Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, Stefano Ermon:
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery. CoRR abs/2207.08051 (2022) - [i167]Sara A. Miskovich, Willie Neiswanger, William Colocho, Claudio Emma, Jacqueline Garrahan, Timothy Maxwell, Christopher Mayes, Stefano Ermon, Auralee Edelen, Daniel Ratner:
Bayesian Algorithm Execution for Tuning Particle Accelerator Emittance with Partial Measurements. CoRR abs/2209.04587 (2022) - [i166]Yann Dubois, Tatsunori Hashimoto, Stefano Ermon, Percy Liang:
Improving Self-Supervised Learning by Characterizing Idealized Representations. CoRR abs/2209.06235 (2022) - [i165]Bahjat Kawar, Jiaming Song, Stefano Ermon, Michael Elad:
JPEG Artifact Correction using Denoising Diffusion Restoration Models. CoRR abs/2209.11888 (2022) - [i164]Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon:
ButterflyFlow: Building Invertible Layers with Butterfly Matrices. CoRR abs/2209.13774 (2022) - [i163]Gengchen Mai, Chiyu Jiang, Weiwei Sun, Rui Zhu, Yao Xuan, Ling Cai, Krzysztof Janowicz, Stefano Ermon, Ni Lao:
Towards General-Purpose Representation Learning of Polygonal Geometries. CoRR abs/2209.15458 (2022) - [i162]Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon:
Generalizing Bayesian Optimization with Decision-theoretic Entropies. CoRR abs/2210.01383 (2022) - [i161]Chenlin Meng, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans:
On Distillation of Guided Diffusion Models. CoRR abs/2210.03142 (2022) - [i160]Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon:
Regularizing Score-based Models with Score Fokker-Planck Equations. CoRR abs/2210.04296 (2022) - [i159]Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger:
Exploration via Planning for Information about the Optimal Trajectory. CoRR abs/2210.04642 (2022) - [i158]Kristy Choi, Chris Cundy, Sanjari Srivastava, Stefano Ermon:
LMPriors: Pre-Trained Language Models as Task-Specific Priors. CoRR abs/2210.12530 (2022) - [i157]Chenlin Meng, Kristy Choi, Jiaming Song, Stefano Ermon:
Concrete Score Matching: Generalized Score Matching for Discrete Data. CoRR abs/2211.00802 (2022) - [i156]Muyang Li, Ji Lin, Chenlin Meng, Stefano Ermon, Song Han, Jun-Yan Zhu:
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models. CoRR abs/2211.02048 (2022) - [i155]Michael Poli, Stefano Massaroli, Federico Berto, Jinkyoo Park, Tri Dao, Christopher Ré, Stefano Ermon:
Transform Once: Efficient Operator Learning in Frequency Domain. CoRR abs/2211.14453 (2022) - [i154]Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, Stefano Ermon:
Deep Latent State Space Models for Time-Series Generation. CoRR abs/2212.12749 (2022) - 2021
- [j6]Jihyeon Janel Lee, Nina R. Brooks, Fahim Tajwar, Marshall Burke, Stefano Ermon, David B. Lobell, Debashish Biswas, Stephen P. Luby:
Scalable deep learning to identify brick kilns and aid regulatory capacity. Proc. Natl. Acad. Sci. USA 118(17): e2018863118 (2021) - [c169]Kumar Ayush, Burak Uzkent, Kumar Tanmay, Marshall Burke, David B. Lobell, Stefano Ermon:
Efficient Poverty Mapping from High Resolution Remote Sensing Images. AAAI 2021: 12-20 - [c168]Jihyeon Janel Lee, Dylan Grosz, Burak Uzkent, Sicheng Zeng, Marshall Burke, David B. Lobell, Stefano Ermon:
Predicting Livelihood Indicators from Community-Generated Street-Level Imagery. AAAI 2021: 268-276 - [c167]Shengjia Zhao, Stefano Ermon:
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration. AISTATS 2021: 2683-2691 - [c166]Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David B. Lobell, Stefano Ermon:
Geography-Aware Self-Supervised Learning. ICCV 2021: 10161-10170 - [c165]Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole:
Score-Based Generative Modeling through Stochastic Differential Equations. ICLR 2021 - [c164]Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon:
Improved Autoregressive Modeling with Distribution Smoothing. ICLR 2021 - [c163]Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh:
On the Critical Role of Conventions in Adaptive Human-AI Collaboration. ICLR 2021 - [c162]Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon:
Negative Data Augmentation. ICLR 2021 - [c161]Jiaming Song, Chenlin Meng, Stefano Ermon:
Denoising Diffusion Implicit Models. ICLR 2021 - [c160]Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon:
Anytime Sampling for Autoregressive Models via Ordered Autoencoding. ICLR 2021 - [c159]Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar E. Carlsson, Stefano Ermon:
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. ICLR 2021 - [c158]Kuno Kim, Shivam Garg, Kirankumar Shiragur, Stefano Ermon:
Reward Identification in Inverse Reinforcement Learning. ICML 2021: 5496-5505 - [c157]Willie Neiswanger, Ke Alexander Wang, Stefano Ermon:
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information. ICML 2021: 8005-8015 - [c156]Tung D. Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon:
Temporal Predictive Coding For Model-Based Planning In Latent Space. ICML 2021: 8130-8139 - [c155]Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon:
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving. ICML 2021: 9791-9800 - [c154]Laure Berti-Équille, David Dao, Stefano Ermon, Bedharta Goswami:
Challenges in KDD and ML for Sustainable Development. KDD 2021: 4031-4032 - [c153]Yang Song, Conor Durkan, Iain Murray, Stefano Ermon:
Maximum Likelihood Training of Score-Based Diffusion Models. NeurIPS 2021: 1415-1428 - [c152]Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon:
Reliable Decisions with Threshold Calibration. NeurIPS 2021: 1831-1844 - [c151]Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Stefano Ermon:
IQ-Learn: Inverse soft-Q Learning for Imitation. NeurIPS 2021: 4028-4039 - [c150]Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon:
Imitation with Neural Density Models. NeurIPS 2021: 5360-5372 - [c149]Chris Cundy, Aditya Grover, Stefano Ermon:
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery. NeurIPS 2021: 7095-7110 - [c148]Andy Shih, Dorsa Sadigh, Stefano Ermon:
HyperSPNs: Compact and Expressive Probabilistic Circuits. NeurIPS 2021: 8571-8582 - [c147]Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon:
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation. NeurIPS 2021: 12533-12548 - [c146]Robin M. E. Swezey, Aditya Grover, Bruno Charron, Stefano Ermon:
PiRank: Scalable Learning To Rank via Differentiable Sorting. NeurIPS 2021: 21644-21654 - [c145]Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon:
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. NeurIPS 2021: 22313-22324 - [c144]Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon:
Pseudo-Spherical Contrastive Divergence. NeurIPS 2021: 22348-22362 - [c143]Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon:
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. NeurIPS 2021: 24804-24816 - [c142]Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon:
Estimating High Order Gradients of the Data Distribution by Denoising. NeurIPS 2021: 25359-25369 - [c141]Mike Wu, Noah D. Goodman, Stefano Ermon:
Improving Compositionality of Neural Networks by Decoding Representations to Inputs. NeurIPS 2021: 26689-26700 - [c140]Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon:
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis. NeurIPS 2021: 27903-27915 - [c139]Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Janel Lee, Marshall Burke, David B. Lobell, Stefano Ermon:
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning. NeurIPS Datasets and Benchmarks 2021 - [c138]Hongwei Wang, Lantao Yu, Zhangjie Cao, Stefano Ermon:
Multi-agent Imitation Learning with Copulas. ECML/PKDD (1) 2021: 139-156 - [c137]Kristy Choi, Madeline Liao, Stefano Ermon:
Featurized density ratio estimation. UAI 2021: 172-182 - [i153]Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon:
Negative Data Augmentation. CoRR abs/2102.05113 (2021) - [i152]Berivan Isik, Kristy Choi, Xin Zheng, Tsachy Weissman, Stefano Ermon, H.-S. Philip Wong, Armin Alaghi:
Neural Network Compression for Noisy Storage Devices. CoRR abs/2102.07725 (2021) - [i151]Rachel Luo, Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai, Shengjia Zhao, Stefano Ermon:
Localized Calibration: Metrics and Recalibration. CoRR abs/2102.10809 (2021) - [i150]Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon:
Anytime Sampling for Autoregressive Models via Ordered Autoencoding. CoRR abs/2102.11495 (2021) - [i149]Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon:
Improved Autoregressive Modeling with Distribution Smoothing. CoRR abs/2103.15089 (2021) - [i148]Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh:
On the Critical Role of Conventions in Adaptive Human-AI Collaboration. CoRR abs/2104.02871 (2021) - [i147]Willie Neiswanger, Ke Alexander Wang, Stefano Ermon:
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information. CoRR abs/2104.09460 (2021) - [i146]Mike Wu, Noah D. Goodman, Stefano Ermon:
Improving Compositionality of Neural Networks by Decoding Representations to Inputs. CoRR abs/2106.00769 (2021) - [i145]Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon:
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation. CoRR abs/2106.06819 (2021) - [i144]Tung D. Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon:
Temporal Predictive Coding For Model-Based Planning In Latent Space. CoRR abs/2106.07156 (2021) - [i143]Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon:
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis. CoRR abs/2106.11485 (2021) - [i142]Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Stefano Ermon:
IQ-Learn: Inverse soft-Q Learning for Imitation. CoRR abs/2106.12142 (2021) - [i141]Kristy Choi, Madeline Liao, Stefano Ermon:
Featurized Density Ratio Estimation. CoRR abs/2107.02212 (2021) - [i140]Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon:
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. CoRR abs/2107.03502 (2021) - [i139]Hongwei Wang, Lantao Yu, Zhangjie Cao, Stefano Ermon:
Multi-Agent Imitation Learning with Copulas. CoRR abs/2107.04750 (2021) - [i138]Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon:
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. CoRR abs/2107.05719 (2021) - [i137]Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon:
SDEdit: Image Synthesis and Editing with Stochastic Differential Equations. CoRR abs/2108.01073 (2021) - [i136]Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ B. Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren E. Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, et al.:
On the Opportunities and Risks of Foundation Models. CoRR abs/2108.07258 (2021) - [i135]Fan-Yun Sun, Jonathan Kuck, Hao Tang, Stefano Ermon:
Equivariant Neural Network for Factor Graphs. CoRR abs/2109.14218 (2021) - [i134]Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon:
Pseudo-Spherical Contrastive Divergence. CoRR abs/2111.00780 (2021) - [i133]Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Janel Lee, Marshall Burke, David B. Lobell, Stefano Ermon:
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning. CoRR abs/2111.04724 (2021) - [i132]Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon:
Estimating High Order Gradients of the Data Distribution by Denoising. CoRR abs/2111.04726 (2021) - [i131]Yang Song, Liyue Shen, Lei Xing, Stefano Ermon:
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models. CoRR abs/2111.08005 (2021) - [i130]Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon:
Density Ratio Estimation via Infinitesimal Classification. CoRR abs/2111.11010 (2021) - [i129]Andy Shih, Dorsa Sadigh, Stefano Ermon:
HyperSPNs: Compact and Expressive Probabilistic Circuits. CoRR abs/2112.00914 (2021) - [i128]Chris Cundy, Aditya Grover, Stefano Ermon:
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery. CoRR abs/2112.02761 (2021) - [i127]Lantao Yu, Yujia Jin, Stefano Ermon:
A Unified Framework for Multi-distribution Density Ratio Estimation. CoRR abs/2112.03440 (2021) - [i126]Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger:
An Experimental Design Perspective on Model-Based Reinforcement Learning. CoRR abs/2112.05244 (2021) - [i125]Volodymyr Kuleshov, Evgenii Nikishin, Shantanu Thakoor, Tingfung Lau, Stefano Ermon:
Quantifying and Understanding Adversarial Examples in Discrete Input Spaces. CoRR abs/2112.06276 (2021) - [i124]Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David B. Lobell, Stefano Ermon:
IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling. CoRR abs/2112.09126 (2021) - 2020
- [j5]Peter M. Attia, Aditya Grover, Norman Jin, Kristen A. Severson, Todor M. Markov, Yang-Hung Liao, Michael H. Chen, Bryan Cheong, Nicholas Perkins, Zi Yang, Patrick K. Herring, Muratahan Aykol, Stephen J. Harris, Richard D. Braatz, Stefano Ermon, William C. Chueh:
Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nat. 578(7795): 397-402 (2020) - [c136]Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon:
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows. AAAI 2020: 4028-4035 - [c135]Mike Wu, Kristy Choi, Noah D. Goodman, Stefano Ermon:
Meta-Amortized Variational Inference and Learning. AAAI 2020: 6404-6412 - [c134]Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon:
Gaussianization Flows. AISTATS 2020: 4336-4345 - [c133]Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon:
Permutation Invariant Graph Generation via Score-Based Generative Modeling. AISTATS 2020: 4474-4484 - [c132]Shengjia Zhao, Christopher Yeh, Stefano Ermon:
A Framework for Sample Efficient Interval Estimation with Control Variates. AISTATS 2020: 4583-4592 - [c131]Nate Gruver, Jiaming Song, Mykel J. Kochenderfer, Stefano Ermon:
Multi-agent Adversarial Inverse Reinforcement Learning with Latent Variables. AAMAS 2020: 1855-1857 - [c130]Han Lin Aung, Burak Uzkent, Marshall Burke, David B. Lobell, Stefano Ermon:
Farm Parcel Delineation Using Spatio-temporal Convolutional Networks. CVPR Workshops 2020: 340-349 - [c129]Burak Uzkent, Stefano Ermon:
Learning When and Where to Zoom With Deep Reinforcement Learning. CVPR 2020: 12342-12351 - [c128]Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole:
Weakly Supervised Disentanglement with Guarantees. ICLR 2020 - [c127]Jiaming Song, Stefano Ermon:
Understanding the Limitations of Variational Mutual Information Estimators. ICLR 2020 - [c126]Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon:
A Theory of Usable Information under Computational Constraints. ICLR 2020 - [c125]Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon:
Fair Generative Modeling via Weak Supervision. ICML 2020: 1887-1898 - [c124]Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon:
Domain Adaptive Imitation Learning. ICML 2020: 5286-5295 - [c123]Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui:
Predictive Coding for Locally-Linear Control. ICML 2020: 8862-8871 - [c122]Jiaming Song, Stefano Ermon:
Bridging the Gap Between f-GANs and Wasserstein GANs. ICML 2020: 9078-9087 - [c121]Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Training Deep Energy-Based Models with f-Divergence Minimization. ICML 2020: 10957-10967 - [c120]Shengjia Zhao, Tengyu Ma, Stefano Ermon:
Individual Calibration with Randomized Forecasting. ICML 2020: 11387-11397 - [c119]Kumar Ayush, Burak Uzkent, Marshall Burke, David B. Lobell, Stefano Ermon:
Generating Interpretable Poverty Maps using Object Detection in Satellite Images. IJCAI 2020: 4410-4416 - [c118]Yang Song, Stefano Ermon:
Improved Techniques for Training Score-Based Generative Models. NeurIPS 2020 - [c117]Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Ré:
HiPPO: Recurrent Memory with Optimal Polynomial Projections. NeurIPS 2020 - [c116]Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon:
Belief Propagation Neural Networks. NeurIPS 2020 - [c115]Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Autoregressive Score Matching. NeurIPS 2020 - [c114]Tianyu Pang, Taufik Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu:
Efficient Learning of Generative Models via Finite-Difference Score Matching. NeurIPS 2020 - [c113]Andy Shih, Stefano Ermon:
Probabilistic Circuits for Variational Inference in Discrete Graphical Models. NeurIPS 2020 - [c112]Jiaming Song, Stefano Ermon:
Multi-label Contrastive Predictive Coding. NeurIPS 2020 - [c111]Yusuke Tashiro, Yang Song, Stefano Ermon:
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks. NeurIPS 2020 - [c110]Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y. Zou, Sergey Levine, Chelsea Finn, Tengyu Ma:
MOPO: Model-based Offline Policy Optimization. NeurIPS 2020 - [c109]Chris Cundy, Stefano Ermon:
Flexible Approximate Inference via Stratified Normalizing Flows. UAI 2020: 1288-1297 - [c108]Vishnu Sarukkai, Anirudh Jain, Burak Uzkent, Stefano Ermon:
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks. WACV 2020: 1785-1794 - [c107]Burak Uzkent, Christopher Yeh, Stefano Ermon:
Efficient Object Detection in Large Images Using Deep Reinforcement Learning. WACV 2020: 1813-1822 - [p1]Hirokazu Narui, Rui Shu, Félix F. González-Navarro, Stefano Ermon:
Domain Adaptation for Human Fall Detection Using WiFi Channel State Information. Precision Health and Medicine 2020: 177-181 - [i123]Kumar Ayush, Burak Uzkent, Marshall Burke, David B. Lobell, Stefano Ermon:
Generating Interpretable Poverty Maps using Object Detection in Satellite Images. CoRR abs/2002.01612 (2020) - [i122]Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon:
Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation. CoRR abs/2002.03629 (2020) - [i121]Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon:
A Theory of Usable Information Under Computational Constraints. CoRR abs/2002.10689 (2020) - [i120]Burak Uzkent, Stefano Ermon:
Learning When and Where to Zoom with Deep Reinforcement Learning. CoRR abs/2003.00425 (2020) - [i119]Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon:
Permutation Invariant Graph Generation via Score-Based Generative Modeling. CoRR abs/2003.00638 (2020) - [i118]Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui:
Predictive Coding for Locally-Linear Control. CoRR abs/2003.01086 (2020) - [i117]Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon:
Gaussianization Flows. CoRR abs/2003.01941 (2020) - [i116]Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Training Deep Energy-Based Models with f-Divergence Minimization. CoRR abs/2003.03463 (2020) - [i115]Yusuke Tashiro, Yang Song, Stefano Ermon:
Output Diversified Initialization for Adversarial Attacks. CoRR abs/2003.06878 (2020) - [i114]Han Lin Aung, Burak Uzkent, Marshall Burke, David B. Lobell, Stefano Ermon:
Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks. CoRR abs/2004.05471 (2020) - [i113]Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma:
MOPO: Model-based Offline Policy Optimization. CoRR abs/2005.13239 (2020) - [i112]Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Stefano Ermon:
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. CoRR abs/2006.03680 (2020) - [i111]Kumar Ayush, Burak Uzkent, Marshall Burke, David B. Lobell, Stefano Ermon:
Efficient Poverty Mapping using Deep Reinforcement Learning. CoRR abs/2006.04224 (2020) - [i110]Jihyeon Janel Lee, Dylan Grosz, Sicheng Zeng, Burak Uzkent, Marshall Burke, David B. Lobell, Stefano Ermon:
Predicting Livelihood Indicators from Crowdsourced Street Level Images. CoRR abs/2006.08661 (2020) - [i109]Yang Song, Stefano Ermon:
Improved Techniques for Training Score-Based Generative Models. CoRR abs/2006.09011 (2020) - [i108]Shengjia Zhao, Christopher Yeh, Stefano Ermon:
A Framework for Sample Efficient Interval Estimation with Control Variates. CoRR abs/2006.10287 (2020) - [i107]Shengjia Zhao, Tengyu Ma, Stefano Ermon:
Individual Calibration with Randomized Forecasting. CoRR abs/2006.10288 (2020) - [i106]Samarth Sinha, Jiaming Song, Animesh Garg, Stefano Ermon:
Experience Replay with Likelihood-free Importance Weights. CoRR abs/2006.13169 (2020) - [i105]Anusri Pampari, Stefano Ermon:
Unsupervised Calibration under Covariate Shift. CoRR abs/2006.16405 (2020) - [i104]Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon:
Belief Propagation Neural Networks. CoRR abs/2007.00295 (2020) - [i103]Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu:
Efficient Learning of Generative Models via Finite-Difference Score Matching. CoRR abs/2007.03317 (2020) - [i102]Jiaming Song, Stefano Ermon:
Multi-label Contrastive Predictive Coding. CoRR abs/2007.09852 (2020) - [i101]Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Ré:
HiPPO: Recurrent Memory with Optimal Polynomial Projections. CoRR abs/2008.07669 (2020) - [i100]Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese:
Privacy Preserving Recalibration under Domain Shift. CoRR abs/2008.09643 (2020) - [i99]Laëtitia Shao, Yang Song, Stefano Ermon:
Understanding Classifier Mistakes with Generative Models. CoRR abs/2010.02364 (2020) - [i98]Jiaming Song, Chenlin Meng, Stefano Ermon:
Denoising Diffusion Implicit Models. CoRR abs/2010.02502 (2020) - [i97]Marshall Burke, Anne Driscoll, David B. Lobell, Stefano Ermon:
Using satellite imagery to understand and promote sustainable development. CoRR abs/2010.06988 (2020) - [i96]Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon:
Imitation with Neural Density Models. CoRR abs/2010.09808 (2020) - [i95]Andy Shih, Stefano Ermon:
Probabilistic Circuits for Variational Inference in Discrete Graphical Models. CoRR abs/2010.11446 (2020) - [i94]Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Autoregressive Score Matching. CoRR abs/2010.12810 (2020) - [i93]Shengjia Zhao, Stefano Ermon:
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration. CoRR abs/2011.07476 (2020) - [i92]Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David B. Lobell, Stefano Ermon:
Geography-Aware Self-Supervised Learning. CoRR abs/2011.09980 (2020) - [i91]Shuvam Chakraborty, Burak Uzkent, Kumar Ayush, Kumar Tanmay, Evan Sheehan, Stefano Ermon:
Efficient Conditional Pre-training for Transfer Learning. CoRR abs/2011.10231 (2020) - [i90]Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole:
Score-Based Generative Modeling through Stochastic Differential Equations. CoRR abs/2011.13456 (2020) - [i89]Robin M. E. Swezey, Aditya Grover, Bruno Charron, Stefano Ermon:
PiRank: Learning To Rank via Differentiable Sorting. CoRR abs/2012.06731 (2020) - [i88]Chris Cundy, Stefano Ermon:
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients. CoRR abs/2012.15019 (2020)
2010 – 2019
- 2019
- [j4]Carla P. Gomes, Thomas G. Dietterich, Christopher Barrett, Jon Conrad, Bistra Dilkina, Stefano Ermon, Fei Fang, Andrew Farnsworth, Alan Fern, Xiaoli Z. Fern, Daniel Fink, Douglas H. Fisher, Alexander Flecker, Daniel Freund, Angela Fuller, John M. Gregoire, John E. Hopcroft, Steve Kelling, J. Zico Kolter, Warren B. Powell, Nicole D. Sintov, John S. Selker, Bart Selman, Daniel Sheldon, David B. Shmoys, Milind Tambe, Weng-Keen Wong, Christopher Wood, Xiaojian Wu, Yexiang Xue, Amulya Yadav, Abdul-Aziz Yakubu, Mary Lou Zeeman:
Computational sustainability: computing for a better world and a sustainable future. Commun. ACM 62(9): 56-65 (2019) - [c106]Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David B. Lobell, Stefano Ermon:
Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data. AAAI 2019: 3967-3974 - [c105]Shengjia Zhao, Jiaming Song, Stefano Ermon:
InfoVAE: Balancing Learning and Inference in Variational Autoencoders. AAAI 2019: 5885-5892 - [c104]Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert, Paul Novosad, Samuel Asher, Zhongyi Tang, Marshall Burke, David B. Lobell, Stefano Ermon:
Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery. AIES 2019: 353-359 - [c103]Rui Shu, Hung H. Bui, Jay Whang, Stefano Ermon:
Training Variational Autoencoders with Buffered Stochastic Variational Inference. AISTATS 2019: 2134-2143 - [c102]Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon:
Learning Controllable Fair Representations. AISTATS 2019: 2164-2173 - [c101]Aditya Grover, Stefano Ermon:
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization. AISTATS 2019: 2514-2524 - [c100]Mike Wu, Noah D. Goodman, Stefano Ermon:
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference. AISTATS 2019: 2877-2886 - [c99]Rose M. Rustowicz, Robin Cheong, Lijing Wang, Stefano Ermon, Marshall Burke, David B. Lobell:
Semantic Segmentation of Crop Type in Africa: A Novel Dataset and Analysis of Deep Learning Methods. CVPR Workshops 2019: 75-82 - [c98]Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon:
AlignFlow: Learning from multiple domains via normalizing flows. DGS@ICLR 2019 - [c97]Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon:
Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting. DGS@ICLR 2019 - [c96]Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon:
Stochastic Optimization of Sorting Networks via Continuous Relaxations. ICLR (Poster) 2019 - [c95]Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon:
Learning Neural PDE Solvers with Convergence Guarantees. ICLR (Poster) 2019 - [c94]Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon:
Neural Joint Source-Channel Coding. ICML 2019: 1182-1192 - [c93]Aditya Grover, Aaron Zweig, Stefano Ermon:
Graphite: Iterative Generative Modeling of Graphs. ICML 2019: 2434-2444 - [c92]Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon:
Calibrated Model-Based Deep Reinforcement Learning. ICML 2019: 4314-4323 - [c91]Hongyu Ren, Shengjia Zhao, Stefano Ermon:
Adaptive Antithetic Sampling for Variance Reduction. ICML 2019: 5420-5428 - [c90]Lantao Yu, Jiaming Song, Stefano Ermon:
Multi-Agent Adversarial Inverse Reinforcement Learning. ICML 2019: 7194-7201 - [c89]Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David B. Lobell, Stefano Ermon:
Learning to Interpret Satellite Images using Wikipedia. IJCAI 2019: 3620-3626 - [c88]Sang Michael Xie, Stefano Ermon:
Reparameterizable Subset Sampling via Continuous Relaxations. IJCAI 2019: 3919-3925 - [c87]Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, Marshall Burke, David B. Lobell, Stefano Ermon:
Predicting Economic Development using Geolocated Wikipedia Articles. KDD 2019: 2698-2706 - [c86]Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon:
Approximating the Permanent by Sampling from Adaptive Partitions. NeurIPS 2019: 8858-8869 - [c85]Sawyer Birnbaum, Volodymyr Kuleshov, S. Zayd Enam, Pang Wei Koh, Stefano Ermon:
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. NeurIPS 2019: 10287-10298 - [c84]Yang Song, Chenlin Meng, Stefano Ermon:
MintNet: Building Invertible Neural Networks with Masked Convolutions. NeurIPS 2019: 11002-11012 - [c83]Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon:
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting. NeurIPS 2019: 11056-11068 - [c82]Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon:
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables. NeurIPS 2019: 11749-11760 - [c81]Yang Song, Stefano Ermon:
Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS 2019: 11895-11907 - [c80]Jonathan Kuck, Tri Dao, Shenjia Zhao, Burak Bartan, Ashish Sabharwal, Stefano Ermon:
Adaptive Hashing for Model Counting. UAI 2019: 271-280 - [c79]Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon:
Sliced Score Matching: A Scalable Approach to Density and Score Estimation. UAI 2019: 574-584 - [i87]Chi-Sing Ho, Neal Jean, Catherine A. Hogan, Lena Blackmon, Stefanie S. Jeffrey, Mark Holodniy, Niaz Banaei, Amr A. E. Saleh, Stefano Ermon, Jennifer A. Dionne:
Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. CoRR abs/1901.07666 (2019) - [i86]Sang Michael Xie, Stefano Ermon:
Differentiable Subset Sampling. CoRR abs/1901.10517 (2019) - [i85]Kristy Choi, Mike Wu, Noah D. Goodman, Stefano Ermon:
Meta-Amortized Variational Inference and Learning. CoRR abs/1902.01950 (2019) - [i84]Rui Shu, Hung H. Bui, Jay Whang, Stefano Ermon:
Training Variational Autoencoders with Buffered Stochastic Variational Inference. CoRR abs/1902.10294 (2019) - [i83]Anthony Perez, Swetava Ganguli, Stefano Ermon, George Azzari, Marshall Burke, David B. Lobell:
Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty. CoRR abs/1902.11110 (2019) - [i82]Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon:
Stochastic Optimization of Sorting Networks via Continuous Relaxations. CoRR abs/1903.08850 (2019) - [i81]Xiao Chen, Thomas Navidi, Stefano Ermon, Ram Rajagopal:
Distributed generation of privacy preserving data with user customization. CoRR abs/1904.09415 (2019) - [i80]Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, David B. Lobell, Marshall Burke, Stefano Ermon:
Predicting Economic Development using Geolocated Wikipedia Articles. CoRR abs/1905.01627 (2019) - [i79]Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert, Paul Novosad, Samuel Asher, Zhongyi Tang, Marshall Burke, David B. Lobell, Stefano Ermon:
Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery. CoRR abs/1905.02196 (2019) - [i78]Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David B. Lobell, Stefano Ermon:
Learning to Interpret Satellite Images in Global Scale Using Wikipedia. CoRR abs/1905.02506 (2019) - [i77]Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon:
Sliced Score Matching: A Scalable Approach to Density and Score Estimation. CoRR abs/1905.07088 (2019) - [i76]Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon:
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows. CoRR abs/1905.12892 (2019) - [i75]Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon:
Learning Neural PDE Solvers with Convergence Guarantees. CoRR abs/1906.01200 (2019) - [i74]Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon:
Calibrated Model-Based Deep Reinforcement Learning. CoRR abs/1906.08312 (2019) - [i73]Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, Stefano Ermon:
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting. CoRR abs/1906.09531 (2019) - [i72]Yang Song, Stefano Ermon:
Generative Modeling by Estimating Gradients of the Data Distribution. CoRR abs/1907.05600 (2019) - [i71]Yang Song, Chenlin Meng, Stefano Ermon:
MintNet: Building Invertible Neural Networks with Masked Convolutions. CoRR abs/1907.07945 (2019) - [i70]Lantao Yu, Jiaming Song, Stefano Ermon:
Multi-Agent Adversarial Inverse Reinforcement Learning. CoRR abs/1907.13220 (2019) - [i69]Sawyer Birnbaum, Volodymyr Kuleshov, S. Zayd Enam, Pang Wei Koh, Stefano Ermon:
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. CoRR abs/1909.06628 (2019) - [i68]Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon:
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables. CoRR abs/1909.09314 (2019) - [i67]Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon:
Cross Domain Imitation Learning. CoRR abs/1910.00105 (2019) - [i66]Jiaming Song, Stefano Ermon:
Understanding the Limitations of Variational Mutual Information Estimators. CoRR abs/1910.06222 (2019) - [i65]Jiaming Song, Yang Song, Stefano Ermon:
Unsupervised Out-of-Distribution Detection with Batch Normalization. CoRR abs/1910.09115 (2019) - [i64]Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole:
Weakly Supervised Disentanglement with Guarantees. CoRR abs/1910.09772 (2019) - [i63]Jiaming Song, Stefano Ermon:
Bridging the Gap Between $f$-GANs and Wasserstein GANs. CoRR abs/1910.09779 (2019) - [i62]Aditya Grover, Kristy Choi, Rui Shu, Stefano Ermon:
Fair Generative Modeling via Weak Supervision. CoRR abs/1910.12008 (2019) - [i61]Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon:
Approximating the Permanent by Sampling from Adaptive Partitions. CoRR abs/1911.11856 (2019) - [i60]Burak Uzkent, Christopher Yeh, Stefano Ermon:
Efficient Object Detection in Large Images using Deep Reinforcement Learning. CoRR abs/1912.03966 (2019) - [i59]Vishnu Sarukkai, Anirudh Jain, Burak Uzkent, Stefano Ermon:
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks. CoRR abs/1912.06838 (2019) - [i58]Y. Alex Kolchinski, Sharon Zhou, Shengjia Zhao, Mitchell L. Gordon, Stefano Ermon:
Approximating Human Judgment of Generated Image Quality. CoRR abs/1912.12121 (2019) - 2018
- [j3]Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon:
Learning with Weak Supervision from Physics and Data-Driven Constraints. AI Mag. 39(1): 27-38 (2018) - [c78]Aditya Grover, Manik Dhar, Stefano Ermon:
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models. AAAI 2018: 3069-3076 - [c77]Aditya Grover, Stefano Ermon:
Boosted Generative Models. AAAI 2018: 3077-3084 - [c76]Daniel Levy, Stefano Ermon:
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces. AAAI 2018: 3474-3481 - [c75]Jonathan Kuck, Ashish Sabharwal, Stefano Ermon:
Approximate Inference via Weighted Rademacher Complexity. AAAI 2018: 6376-6383 - [c74]Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon:
Variational Rejection Sampling. AISTATS 2018: 823-832 - [c73]Aditya Grover, Todor M. Markov, Peter M. Attia, Norman Jin, Nicolas Perkins, Bryan Cheong, Michael H. Chen, Zi Yang, Stephen J. Harris, William C. Chueh, Stefano Ermon:
Best arm identification in multi-armed bandits with delayed feedback. AISTATS 2018: 833-842 - [c72]Lijie Fan, Wen-bing Huang, Chuang Gan, Stefano Ermon, Boqing Gong, Junzhou Huang:
End-to-End Learning of Motion Representation for Video Understanding. CVPR 2018: 6016-6025 - [c71]Anna X. Wang, Caelin Tran, Nikhil Desai, David B. Lobell, Stefano Ermon:
Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data. COMPASS 2018: 50:1-50:5 - [c70]Rui Shu, Hung H. Bui, Hirokazu Narui, Stefano Ermon:
A DIRT-T Approach to Unsupervised Domain Adaptation. ICLR (Poster) 2018 - [c69]Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman:
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples. ICLR (Poster) 2018 - [c68]Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon:
Multi-Agent Generative Adversarial Imitation Learning. ICLR (Workshop) 2018 - [c67]Manik Dhar, Aditya Grover, Stefano Ermon:
Modeling Sparse Deviations for Compressed Sensing using Generative Models. ICML 2018: 1222-1231 - [c66]Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon:
Accurate Uncertainties for Deep Learning Using Calibrated Regression. ICML 2018: 2801-2809 - [c65]Yang Song, Jiaming Song, Stefano Ermon:
Accelerating Natural Gradient with Higher-Order Invariance. ICML 2018: 4720-4729 - [c64]Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon:
Adversarial Constraint Learning for Structured Prediction. IJCAI 2018: 2637-2643 - [c63]Barak Oshri, Annie Hu, Peter Adelson, Xiao Chen, Pascaline Dupas, Jeremy Weinstein, Marshall Burke, David B. Lobell, Stefano Ermon:
Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning. KDD 2018: 616-625 - [c62]Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon:
Amortized Inference Regularization. NeurIPS 2018: 4398-4407 - [c61]Neal Jean, Sang Michael Xie, Stefano Ermon:
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. NeurIPS 2018: 5327-5338 - [c60]Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon:
Multi-Agent Generative Adversarial Imitation Learning. NeurIPS 2018: 7472-7483 - [c59]Yang Song, Rui Shu, Nate Kushman, Stefano Ermon:
Constructing Unrestricted Adversarial Examples with Generative Models. NeurIPS 2018: 8322-8333 - [c58]Aditya Grover, Tudor Achim, Stefano Ermon:
Streamlining Variational Inference for Constraint Satisfaction Problems. NeurIPS 2018: 10579-10589 - [c57]Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah D. Goodman, Stefano Ermon:
Bias and Generalization in Deep Generative Models: An Empirical Study. NeurIPS 2018: 10815-10824 - [c56]Shengjia Zhao, Jiaming Song, Stefano Ermon:
A Lagrangian Perspective on Latent Variable Generative Models. UAI 2018: 1031-1041 - [c55]Stephan Eismann, Daniel Levy, Rui Shu, Stefan Bartzsch, Stefano Ermon:
Bayesian optimization and attribute adjustment. UAI 2018: 1042-1052 - [i57]Jonathan Kuck, Ashish Sabharwal, Stefano Ermon:
Approximate Inference via Weighted Rademacher Complexity. CoRR abs/1801.09028 (2018) - [i56]Rui Shu, Hung H. Bui, Hirokazu Narui, Stefano Ermon:
A DIRT-T Approach to Unsupervised Domain Adaptation. CoRR abs/1802.08735 (2018) - [i55]Yang Song, Stefano Ermon:
Accelerating Natural Gradient with Higher-Order Invariance. CoRR abs/1803.01273 (2018) - [i54]Aditya Grover, Aaron Zweig, Stefano Ermon:
Graphite: Iterative Generative Modeling of Graphs. CoRR abs/1803.10459 (2018) - [i53]Aditya Grover, Todor M. Markov, Peter M. Attia, Norman Jin, Nicholas Perkins, Bryan Cheong, Michael H. Chen, Zi Yang, Stephen J. Harris, William C. Chueh, Stefano Ermon:
Best arm identification in multi-armed bandits with delayed feedback. CoRR abs/1803.10937 (2018) - [i52]Lijie Fan, Wen-bing Huang, Chuang Gan, Stefano Ermon, Boqing Gong, Junzhou Huang:
End-to-End Learning of Motion Representation for Video Understanding. CoRR abs/1804.00413 (2018) - [i51]Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon:
Variational Rejection Sampling. CoRR abs/1804.01712 (2018) - [i50]Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David B. Lobell, Stefano Ermon:
Tile2Vec: Unsupervised representation learning for spatially distributed data. CoRR abs/1805.02855 (2018) - [i49]Yang Song, Rui Shu, Nate Kushman, Stefano Ermon:
Generative Adversarial Examples. CoRR abs/1805.07894 (2018) - [i48]Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon:
Amortized Inference Regularization. CoRR abs/1805.08913 (2018) - [i47]Neal Jean, Sang Michael Xie, Stefano Ermon:
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. CoRR abs/1805.10407 (2018) - [i46]Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon:
Adversarial Constraint Learning for Structured Prediction. CoRR abs/1805.10561 (2018) - [i45]Barak Oshri, Annie Hu, Peter Adelson, Xiao Chen, Pascaline Dupas, Jeremy Weinstein, Marshall Burke, David B. Lobell, Stefano Ermon:
Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning. CoRR abs/1806.00894 (2018) - [i44]Shengjia Zhao, Jiaming Song, Stefano Ermon:
The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models. CoRR abs/1806.06514 (2018) - [i43]Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon:
Accurate Uncertainties for Deep Learning Using Calibrated Regression. CoRR abs/1807.00263 (2018) - [i42]Manik Dhar, Aditya Grover, Stefano Ermon:
Modeling Sparse Deviations for Compressed Sensing using Generative Models. CoRR abs/1807.01442 (2018) - [i41]Rishi Sharma, Shane T. Barratt, Stefano Ermon, Vijay S. Pande:
Improved Training with Curriculum GANs. CoRR abs/1807.09295 (2018) - [i40]Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon:
Multi-Agent Generative Adversarial Imitation Learning. CoRR abs/1807.09936 (2018) - [i39]Evan Sheehan, Burak Uzkent, Chenlin Meng, Zhongyi Tang, Marshall Burke, David B. Lobell, Stefano Ermon:
Learning to Interpret Satellite Images Using Wikipedia. CoRR abs/1809.10236 (2018) - [i38]Mike Wu, Noah D. Goodman, Stefano Ermon:
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference. CoRR abs/1810.02555 (2018) - [i37]Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah D. Goodman, Stefano Ermon:
Bias and Generalization in Deep Generative Models: An Empirical Study. CoRR abs/1811.03259 (2018) - [i36]Kristy Choi, Kedar Tatwawadi, Tsachy Weissman, Stefano Ermon:
NECST: Neural Joint Source-Channel Coding. CoRR abs/1811.07557 (2018) - [i35]Aditya Grover, Tudor Achim, Stefano Ermon:
Streamlining Variational Inference for Constraint Satisfaction Problems. CoRR abs/1811.09813 (2018) - [i34]Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon:
Learning Controllable Fair Representations. CoRR abs/1812.04218 (2018) - [i33]Aditya Grover, Stefano Ermon:
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization. CoRR abs/1812.10539 (2018) - 2017
- [j2]Siamak Yousefi, Hirokazu Narui, Sankalp Dayal, Stefano Ermon, Shahrokh Valaee:
A Survey on Behavior Recognition Using WiFi Channel State Information. IEEE Commun. Mag. 55(10): 98-104 (2017) - [c54]Volodymyr Kuleshov, Stefano Ermon:
Estimating Uncertainty Online Against an Adversary. AAAI 2017: 2110-2116 - [c53]Russell Stewart, Stefano Ermon:
Label-Free Supervision of Neural Networks with Physics and Domain Knowledge. AAAI 2017: 2576-2582 - [c52]Colin Wei, Stefano Ermon:
General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis. AAAI 2017: 3958-3966 - [c51]Jiaxuan You, Xiaocheng Li, Melvin Low, David B. Lobell, Stefano Ermon:
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data. AAAI 2017: 4559-4566 - [c50]Reid Pryzant, Stefano Ermon, David B. Lobell:
Monitoring Ethiopian Wheat Fungus with Satellite Imagery and Deep Feature Learning. CVPR Workshops 2017: 1524-1532 - [c49]Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon:
Audio Super-Resolution using Neural Networks. ICLR (Workshop) 2017 - [c48]Jiaming Song, Shengjia Zhao, Stefano Ermon:
Generative Adversarial Learning of Markov Chains. ICLR (Workshop) 2017 - [c47]Shengjia Zhao, Jiaming Song, Stefano Ermon:
Learning Hierarchical Features from Deep Generative Models. ICML 2017: 4091-4099 - [c46]Biagio Cosenza, Juan José Durillo, Stefano Ermon, Ben H. H. Juurlink:
Autotuning Stencil Computations with Structural Ordinal Regression Learning. IPDPS 2017: 287-296 - [c45]Yunzhu Li, Jiaming Song, Stefano Ermon:
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations. NIPS 2017: 3812-3822 - [c44]Jiaming Song, Shengjia Zhao, Stefano Ermon:
A-NICE-MC: Adversarial Training for MCMC. NIPS 2017: 5140-5150 - [c43]Volodymyr Kuleshov, Stefano Ermon:
Neural Variational Inference and Learning in Undirected Graphical Models. NIPS 2017: 6734-6743 - [c42]Biagio Cosenza, Juan José Durillo, Stefano Ermon, Ben H. H. Juurlink:
Stencil Autotuning with Ordinal Regression: Extended Abstract. SCOPES 2017: 72-75 - [c41]Volodymyr Kuleshov, Stefano Ermon:
Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning. UAI 2017 - [c40]Stephen Mussmann, Daniel Levy, Stefano Ermon:
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. UAI 2017 - [i32]Colin Wei, Stefano Ermon:
General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis. CoRR abs/1701.06258 (2017) - [i31]Shengjia Zhao, Jiaming Song, Stefano Ermon:
Learning Hierarchical Features from Generative Models. CoRR abs/1702.08396 (2017) - [i30]Aditya Grover, Stefano Ermon:
Boosted Generative Models. CoRR abs/1702.08484 (2017) - [i29]Shengjia Zhao, Jiaming Song, Stefano Ermon:
Towards Deeper Understanding of Variational Autoencoding Models. CoRR abs/1702.08658 (2017) - [i28]Jiaming Song, Russell Stewart, Shengjia Zhao, Stefano Ermon:
On the Limits of Learning Representations with Label-Based Supervision. CoRR abs/1703.02156 (2017) - [i27]Yunzhu Li, Jiaming Song, Stefano Ermon:
Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs. CoRR abs/1703.08840 (2017) - [i26]Aditya Grover, Manik Dhar, Stefano Ermon:
Flow-GAN: Bridging implicit and prescribed learning in generative models. CoRR abs/1705.08868 (2017) - [i25]Shengjia Zhao, Jiaming Song, Stefano Ermon:
InfoVAE: Information Maximizing Variational Autoencoders. CoRR abs/1706.02262 (2017) - [i24]Jiaming Song, Shengjia Zhao, Stefano Ermon:
A-NICE-MC: Adversarial Training for MCMC. CoRR abs/1706.07561 (2017) - [i23]Stephen Mussmann, Daniel Levy, Stefano Ermon:
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. CoRR abs/1707.03372 (2017) - [i22]Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon:
Audio Super Resolution using Neural Networks. CoRR abs/1708.00853 (2017) - [i21]Siamak Yousefi, Hirokazu Narui, Sankalp Dayal, Stefano Ermon, Shahrokh Valaee:
A Survey of Human Activity Recognition Using WiFi CSI. CoRR abs/1708.07129 (2017) - [i20]Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman:
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples. CoRR abs/1710.10766 (2017) - [i19]Volodymyr Kuleshov, Stefano Ermon:
Neural Variational Inference and Learning in Undirected Graphical Models. CoRR abs/1711.02679 (2017) - [i18]Anthony Perez, Christopher Yeh, George Azzari, Marshall Burke, David B. Lobell, Stefano Ermon:
Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning. CoRR abs/1711.03654 (2017) - [i17]Huaiyang Zhong, Xiaocheng Li, David B. Lobell, Stefano Ermon, Margaret L. Brandeau:
Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans. CoRR abs/1711.05809 (2017) - [i16]Daniel Levy, Stefano Ermon:
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces. CoRR abs/1711.08068 (2017) - [i15]Stephan Eismann, Stefan Bartzsch, Stefano Ermon:
Shape optimization in laminar flow with a label-guided variational autoencoder. CoRR abs/1712.03599 (2017) - 2016
- [c39]Carolyn Kim, Ashish Sabharwal, Stefano Ermon:
Exact Sampling with Integer Linear Programs and Random Perturbations. AAAI 2016: 3248-3254 - [c38]Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon:
Closing the Gap Between Short and Long XORs for Model Counting. AAAI 2016: 3322-3329 - [c37]Sang Michael Xie, Neal Jean, Marshall Burke, David B. Lobell, Stefano Ermon:
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. AAAI 2016: 3929-3935 - [c36]Lun-Kai Hsu, Tudor Achim, Stefano Ermon:
Tight Variational Bounds via Random Projections and I-Projections. AISTATS 2016: 1087-1095 - [c35]Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla P. Gomes, Bart Selman:
Variable Elimination in the Fourier Domain. ICML 2016: 285-294 - [c34]Tudor Achim, Ashish Sabharwal, Stefano Ermon:
Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference. ICML 2016: 2254-2262 - [c33]Stephen Mussmann, Stefano Ermon:
Learning and Inference via Maximum Inner Product Search. ICML 2016: 2587-2596 - [c32]Jonathan Ho, Jayesh K. Gupta, Stefano Ermon:
Model-Free Imitation Learning with Policy Optimization. ICML 2016: 2760-2769 - [c31]Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman:
Solving Marginal MAP Problems with NP Oracles and Parity Constraints. NIPS 2016: 1127-1135 - [c30]Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon:
Adaptive Concentration Inequalities for Sequential Decision Problems. NIPS 2016: 1343-1351 - [c29]Aditya Grover, Stefano Ermon:
Variational Bayes on Monte Carlo Steroids. NIPS 2016: 3018-3026 - [c28]Jonathan Ho, Stefano Ermon:
Generative Adversarial Imitation Learning. NIPS 2016: 4565-4573 - [c27]Mitchell McIntire, Daniel Ratner, Stefano Ermon:
Sparse Gaussian Processes for Bayesian Optimization. UAI 2016 - [i14]Jonathan Ho, Jayesh K. Gupta, Stefano Ermon:
Model-Free Imitation Learning with Policy Optimization. CoRR abs/1605.08478 (2016) - [i13]Jonathan Ho, Stefano Ermon:
Generative Adversarial Imitation Learning. CoRR abs/1606.03476 (2016) - [i12]Volodymyr Kuleshov, Stefano Ermon:
Reliable Confidence Estimation via Online Learning. CoRR abs/1607.03594 (2016) - [i11]Russell Stewart, Stefano Ermon:
Label-Free Supervision of Neural Networks with Physics and Domain Knowledge. CoRR abs/1609.05566 (2016) - [i10]Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman:
Solving Marginal MAP Problems with NP Oracles and Parity Constraints. CoRR abs/1610.02591 (2016) - 2015
- [b1]Stefano Ermon:
Decision Making And Inference Under Limited Information And High Dimensionality. Cornell University, USA, 2015 - [c26]Stefano Ermon, Ronan Le Bras, Santosh K. Suram, John M. Gregoire, Carla P. Gomes, Bart Selman, Robert Bruce van Dover:
Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery. AAAI 2015: 636-643 - [c25]Stefano Ermon, Yexiang Xue, Russell Toth, Bistra Dilkina, Richard Bernstein, Theodoros Damoulas, Patrick E. Clark, Steve DeGloria, Andrew Mude, Christopher Barrett, Carla P. Gomes:
Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa. AAAI 2015: 644-650 - [c24]Yexiang Xue, Stefano Ermon, Carla P. Gomes, Bart Selman:
Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem. AAAI Workshop: Computational Sustainability 2015 - [c23]Michael Zhu, Stefano Ermon:
A Hybrid Approach for Probabilistic Inference using Random Projections. ICML 2015: 2039-2047 - [c22]Yexiang Xue, Stefano Ermon, Carla P. Gomes, Bart Selman:
Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem: Application to Materials Discovery. IJCAI 2015: 146-155 - [c21]Stefan Hadjis, Stefano Ermon:
Importance Sampling over Sets: A New Probabilistic Inference Scheme. UAI 2015: 355-364 - [e1]Bistra Dilkina, Stefano Ermon, Rebecca A. Hutchinson, Daniel Sheldon:
Computational Sustainability, Papers from the 2015 AAAI Workshop, Austin, Texas, USA, January 26, 2015. AAAI Technical Report WS-15-06, AAAI Press 2015, ISBN 978-1-57735-717-9 [contents] - [i9]Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla P. Gomes, Bart Selman:
Variable Elimination in Fourier Domain. CoRR abs/1508.04032 (2015) - [i8]Sang Michael Xie, Neal Jean, Marshall Burke, David B. Lobell, Stefano Ermon:
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. CoRR abs/1510.00098 (2015) - [i7]Lun-Kai Hsu, Tudor Achim, Stefano Ermon:
Tight Variational Bounds via Random Projections and I-Projections. CoRR abs/1510.01308 (2015) - [i6]Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon:
Closing the Gap Between Short and Long XORs for Model Counting. CoRR abs/1512.08863 (2015) - 2014
- [c20]Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Designing Fast Absorbing Markov Chains. AAAI 2014: 849-855 - [c19]Yexiang Xue, Stefano Ermon, Carla P. Gomes, Bart Selman:
Uncovering Hidden Structure through Parallel Problem Decomposition. AAAI 2014: 3144-3145 - [c18]Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Low-density Parity Constraints for Hashing-Based Discrete Integration. ICML 2014: 271-279 - [i5]Stefano Ermon, Ronan Le Bras, Santosh K. Suram, John M. Gregoire, Carla P. Gomes, Bart Selman, Robert Bruce van Dover:
Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery. CoRR abs/1411.7441 (2014) - 2013
- [j1]Stefano Ermon, Yexiang Xue, Carla P. Gomes, Bart Selman:
Learning policies for battery usage optimization in electric vehicles. Mach. Learn. 92(1): 177-194 (2013) - [c17]Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization. ICML (2) 2013: 334-342 - [c16]Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Embed and Project: Discrete Sampling with Universal Hashing. NIPS 2013: 2085-2093 - [c15]Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Optimization With Parity Constraints: From Binary Codes to Discrete Integration. UAI 2013 - [i4]Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization. CoRR abs/1302.6677 (2013) - [i3]Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Optimization With Parity Constraints: From Binary Codes to Discrete Integration. CoRR abs/1309.6827 (2013) - 2012
- [c14]Stefano Ermon, Carla P. Gomes, Bart Selman, Alexander Vladimirsky:
Probabilistic planning with non-linear utility functions and worst-case guarantees. AAMAS 2012: 965-972 - [c13]Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Density Propagation and Improved Bounds on the Partition Function. NIPS 2012: 2771-2779 - [c12]Stefano Ermon, Yexiang Xue, Carla P. Gomes, Bart Selman:
Learning Policies for Battery Usage Optimization in Electric Vehicles. ECML/PKDD (2) 2012: 195-210 - [c11]Liaoruo Wang, Stefano Ermon, John E. Hopcroft:
Feature-Enhanced Probabilistic Models for Diffusion Network Inference. ECML/PKDD (2) 2012: 499-514 - [c10]Stefano Ermon, Ronan LeBras, Carla P. Gomes, Bart Selman, R. Bruce van Dover:
SMT-Aided Combinatorial Materials Discovery. SAT 2012: 172-185 - [c9]Stefano Ermon, Carla P. Gomes, Bart Selman:
Uniform Solution Sampling Using a Constraint Solver As an Oracle. UAI 2012: 255-264 - [i2]Stefano Ermon, Jon Conrad, Carla P. Gomes, Bart Selman:
Playing games against nature: optimal policies for renewable resource allocation. CoRR abs/1203.3478 (2012) - [i1]Stefano Ermon, Carla P. Gomes, Bart Selman:
Uniform Solution Sampling Using a Constraint Solver As an Oracle. CoRR abs/1210.4861 (2012) - 2011
- [c8]Stefano Ermon, Carla P. Gomes, Bart Selman:
A message passing approach to multiagent gaussian inference for dynamic processes. AAMAS 2011: 1277-1278 - [c7]Stefano Ermon, Jon Conrad, Carla P. Gomes, Bart Selman:
Risk-Sensitive Policies for Sustainable Renewable Resource Allocation. IJCAI 2011: 1942-1948 - [c6]Stefano Ermon, Carla P. Gomes, Bart Selman:
A Flat Histogram Method for Computing the Density of States of Combinatorial Problems. IJCAI 2011: 2608-2613 - [c5]Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Accelerated Adaptive Markov Chain for Partition Function Computation. NIPS 2011: 2744-2752 - 2010
- [c4]Stefano Ermon, Carla P. Gomes, Bart Selman:
Collaborative multiagent Gaussian inference in a dynamic environment using belief propagation. AAMAS 2010: 1419-1420 - [c3]Stefano Ermon, Carla P. Gomes, Bart Selman:
Computing the Density of States of Boolean Formulas. CP 2010: 38-52 - [c2]Stefano Ermon, Jon Conrad, Carla P. Gomes, Bart Selman:
Playing games against nature: optimal policies for renewable resource allocation. UAI 2010: 168-176
2000 – 2009
- 2009
- [c1]Stefano Ermon, Luca Schenato, Sandro Zampieri:
Trust Estimation in autonomic networks: a statistical mechanics approach. CDC 2009: 4790-4795
Coauthor Index