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ORCIDStefano Ermon
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- affiliation: Stanford University
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2020 – today
- 2023
[i187]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)- [i186]Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon:
Extreme Q-Learning: MaxEnt RL without Entropy. CoRR abs/2301.02328 (2023) - [i185]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) - [i184]Andy Shih, Dorsa Sadigh, Stefano Ermon:
Long Horizon Temperature Scaling. CoRR abs/2302.03686 (2023) - [i183]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) - [i182]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) - 2022
- [c187]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 - [c186]Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon:
Density Ratio Estimation via Infinitesimal Classification. AISTATS 2022: 2552-2573 - [c185]Shuvam Chakraborty, Burak Uzkent, Kumar Ayush, Kumar Tanmay, Evan Sheehan, Stefano Ermon:
Efficient Conditional Pre-training for Transfer Learning. CVPR Workshops 2022: 4240-4249 - [c184]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 - [c183]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 - [c182]Andy Shih, Stefano Ermon, Dorsa Sadigh:
Conditional Imitation Learning for Multi-Agent Games. HRI 2022: 166-175 - [c181]Yang Song, Liyue Shen, Lei Xing, Stefano Ermon:
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models. ICLR 2022 - [c180]Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger:
An Experimental Design Perspective on Model-Based Reinforcement Learning. ICLR 2022 - [c179]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 - [c178]Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang:
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation. ICLR 2022 - [c177]Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon:
Comparing Distributions by Measuring Differences that Affect Decision Making. ICLR 2022 - [c176]Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani:
Imitation Learning by Estimating Expertise of Demonstrators. ICML 2022: 1732-1748 - [c175]Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon:
Modular Conformal Calibration. ICML 2022: 15180-15195 - [c174]Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon:
ButterflyFlow: Building Invertible Layers with Butterfly Matrices. ICML 2022: 15360-15375 - [c173]Rui Shu, Stefano Ermon:
Bit Prioritization in Variational Autoencoders via Progressive Coding. ICML 2022: 20141-20155 - [c172]Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon:
A General Recipe for Likelihood-free Bayesian Optimization. ICML 2022: 20384-20404 - [c171]Samarth Sinha, Jiaming Song, Animesh Garg, Stefano Ermon:
Experience Replay with Likelihood-free Importance Weights. L4DC 2022: 110-123 - [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 - [i181]Andy Shih, Stefano Ermon, Dorsa Sadigh:
Conditional Imitation Learning for Multi-Agent Games. CoRR abs/2201.01448 (2022) - [i180]Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song:
Denoising Diffusion Restoration Models. CoRR abs/2201.11793 (2022) - [i179]Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani:
Imitation Learning by Estimating Expertise of Demonstrators. CoRR abs/2202.01288 (2022) - [i178]Divyansh Garg, Skanda Vaidyanath, Kuno Kim, Jiaming Song, Stefano Ermon:
LISA: Learning Interpretable Skill Abstractions from Language. CoRR abs/2203.00054 (2022) - [i177]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) - [i176]Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon:
Dual Diffusion Implicit Bridges for Image-to-Image Translation. CoRR abs/2203.08382 (2022) - [i175]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) - [i174]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) - [i173]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) - [i172]Andy Shih, Dorsa Sadigh, Stefano Ermon:
Training and Inference on Any-Order Autoregressive Models the Right Way. CoRR abs/2205.13554 (2022) - [i171]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) - [i170]Charles Marx, Shengjia Zhou, 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 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]