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ORCID11th ICLR 2023: Kigali, Rwanda
- The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net 2023
Notable-top-5%
- Feiqing Huang, Kexin Lu, Yuxi Cai, Zhen Qin, Yanwen Fang, Guangjian Tian, Guodong Li:
Encoding Recurrence into Transformers. - Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean:
Modeling content creator incentives on algorithm-curated platforms. - Gresa Shala, Thomas Elsken, Frank Hutter, Josif Grabocka:
Transfer NAS with Meta-learned Bayesian Surrogates. - Anji Liu, Honghua Zhang, Guy Van den Broeck:
Scaling Up Probabilistic Circuits by Latent Variable Distillation. - Daniel Barzilai, Amnon Geifman, Meirav Galun, Ronen Basri:
A Kernel Perspective of Skip Connections in Convolutional Networks. - Matthew Ho, Aditya Sharma, Justin Chang, Michael Saxon
, Sharon Levy, Yujie Lu, William Yang Wang:
WikiWhy: Answering and Explaining Cause-and-Effect Questions. - Samuel K. Ainsworth, Jonathan Hayase, Siddhartha S. Srinivasa:
Git Re-Basin: Merging Models modulo Permutation Symmetries. - Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade:
The Role of Coverage in Online Reinforcement Learning. - Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama:
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. - Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine:
Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes. - Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, Denny Zhou:
What learning algorithm is in-context learning? Investigations with linear models. - Zeyuan Allen-Zhu, Yuanzhi Li:
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning. - Mert Yüksekgönül, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, James Zou:
When and Why Vision-Language Models Behave like Bags-Of-Words, and What to Do About It? - Joey Hong, Aviral Kumar, Sergey Levine:
Confidence-Conditioned Value Functions for Offline Reinforcement Learning. - Joey Hong, Kush Bhatia, Anca D. Dragan:
On the Sensitivity of Reward Inference to Misspecified Human Models. - Jinhyung Park, Chenfeng Xu, Shijia Yang, Kurt Keutzer, Kris M. Kitani, Masayoshi Tomizuka, Wei Zhan:
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection. - Sherry Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum:
Dichotomy of Control: Separating What You Can Control from What You Cannot. - Cristina Cornelio, Jan Stuehmer, Shell Xu Hu, Timothy M. Hospedales:
Learning where and when to reason in neuro-symbolic inference. - Quentin Garrido, Yubei Chen, Adrien Bardes, Laurent Najman, Yann LeCun:
On the duality between contrastive and non-contrastive self-supervised learning. - Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall:
DreamFusion: Text-to-3D using 2D Diffusion. - Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru Zhang:
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. - Donggyun Kim, Jinwoo Kim, Seongwoong Cho, Chong Luo, Seunghoon Hong:
Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching. - Heshan Devaka Fernando, Han Shen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, Tianyi Chen:
Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach. - Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, Yuan Cao:
ReAct: Synergizing Reasoning and Acting in Language Models. - Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, Mehrdad Mahdavi:
Do We Really Need Complicated Model Architectures For Temporal Networks? - Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, Pulkit Agrawal:
Is Conditional Generative Modeling all you need for Decision Making? - Nate Gruver, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson:
The Lie Derivative for Measuring Learned Equivariance. - Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy:
Agree to Disagree: Diversity through Disagreement for Better Transferability. - Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton:
Efficient Conditionally Invariant Representation Learning. - Joel Dapello, Kohitij Kar, Martin Schrimpf, Robert Baldwin Geary, Michael Ferguson, David Daniel Cox, James J. DiCarlo:
Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. - Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang:
Transformers Learn Shortcuts to Automata. - Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan A. Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih:
In-context Reinforcement Learning with Algorithm Distillation. - Antonia Creswell, Murray Shanahan, Irina Higgins:
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning. - Langwen Huang, Torsten Hoefler:
Compressing multidimensional weather and climate data into neural networks. - Ali Shahin Shamsabadi, Sierra Calanda Wyllie, Nicholas Franzese, Natalie Dullerud, Sébastien Gambs, Nicolas Papernot, Xiao Wang, Adrian Weller:
Confidential-PROFITT: Confidential PROof of FaIr Training of Trees. - Lingxiao Huang, Shaofeng H.-C. Jiang, Jianing Lou, Xuan Wu:
Near-optimal Coresets for Robust Clustering. - Shaokun Zhang, Feiran Jia, Chi Wang, Qingyun Wu:
Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives. - Anton Bakhtin, David J. Wu, Adam Lerer, Jonathan Gray, Athul Paul Jacob, Gabriele Farina, Alexander H. Miller, Noam Brown:
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning. - Lin Zheng, Jianbo Yuan, Chong Wang, Lingpeng Kong:
Efficient Attention via Control Variates. - Thomas Möllenhoff, Mohammad Emtiyaz Khan:
SAM as an Optimal Relaxation of Bayes. - Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, Jian Tang:
Learning on Large-scale Text-attributed Graphs via Variational Inference. - Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon:
Extreme Q-Learning: MaxEnt RL without Entropy. - Fivos Kalogiannis, Ioannis Anagnostides, Ioannis Panageas, Emmanouil V. Vlatakis-Gkaragkounis, Vaggos Chatziafratis, Stelios Andrew Stavroulakis:
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games. - Jimmy T. H. Smith, Andrew Warrington, Scott W. Linderman:
Simplified State Space Layers for Sequence Modeling. - Kuo-Hao Zeng, Luca Weihs, Roozbeh Mottaghi, Ali Farhadi:
Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics. - Yuzhe Yang, Xin Liu, Jiang Wu, Silviu Borac, Dina Katabi, Ming-Zher Poh, Daniel McDuff:
SimPer: Simple Self-Supervised Learning of Periodic Targets. - Xi Chen, Xiao Wang, Soravit Changpinyo, A. J. Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Nan Ding, Keran Rong, Hassan Akbari, Gaurav Mishra, Linting Xue, Ashish V. Thapliyal, James Bradbury, Weicheng Kuo:
PaLI: A Jointly-Scaled Multilingual Language-Image Model. - Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G. Bellemare, Aaron C. Courville:
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier. - Shuaichen Chang, Jun Wang, Mingwen Dong, Lin Pan, Henghui Zhu, Alexander Hanbo Li, Wuwei Lan, Sheng Zhang, Jiarong Jiang, Joseph Lilien, Steve Ash, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Patrick Ng, Bing Xiang:
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness. - Guangji Bai, Chen Ling, Liang Zhao:
Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks. - Albert Qiaochu Jiang, Sean Welleck, Jin Peng Zhou, Timothée Lacroix, Jiacheng Liu, Wenda Li, Mateja Jamnik, Guillaume Lample, Yuhuai Wu:
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs. - Duc N. M. Hoang, Shiwei Liu, Radu Marculescu, Zhangyang Wang:
Revisiting Pruning at Initialization Through the Lens of Ramanujan Graph. - Chongyi Li, Chun-Le Guo, Man Zhou, Zhexin Liang, Shangchen Zhou, Ruicheng Feng, Chen Change Loy:
Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement. - Paul F. Jaeger, Carsten T. Lüth, Lukas Klein, Till J. Bungert:
A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification. - Michal Zawalski, Michal Tyrolski, Konrad Czechowski, Tomasz Odrzygózdz, Damian Stachura, Piotr Piekos, Yuhuai Wu, Lukasz Kucinski, Piotr Milos:
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search. - Kaituo Feng, Changsheng Li, Xiaolu Zhang, Jun Zhou:
Towards Open Temporal Graph Neural Networks. - Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, Emanuele Rodolà:
Relative representations enable zero-shot latent space communication. - Phillip Rust, Jonas F. Lotz, Emanuele Bugliarello, Elizabeth Salesky, Miryam de Lhoneux, Desmond Elliott:
Language Modelling with Pixels. - Marius-Constantin Dinu, Markus Holzleitner, Maximilian Beck, Hoan Duc Nguyen, Andrea Huber, Hamid Eghbal-zadeh, Bernhard Alois Moser, Sergei V. Pereverzyev, Sepp Hochreiter, Werner Zellinger:
Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation. - Fangzheng Sun, Yang Liu, Jian-Xun Wang, Hao Sun:
Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search. - Kangjie Chen, Xiaoxuan Lou, Guowen Xu, Jiwei Li, Tianwei Zhang:
Clean-image Backdoor: Attacking Multi-label Models with Poisoned Labels Only. - Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Yannick Hammerla, Michael M. Bronstein, Max Hansmire:
Graph Neural Networks for Link Prediction with Subgraph Sketching. - David Klee, Ondrej Biza, Robert Platt, Robin Walters:
Image to Sphere: Learning Equivariant Features for Efficient Pose Prediction. - Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, Yifei Xiao:
MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting. - Jian Xu, Xinyi Tong, Shao-Lun Huang:
Personalized Federated Learning with Feature Alignment and Classifier Collaboration. - Zichen Jeff Cui, Yibin Wang, Nur Muhammad (Mahi) Shafiullah, Lerrel Pinto:
From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data. - Sachit Menon, Carl Vondrick:
Visual Classification via Description from Large Language Models. - Zihui Xue, Zhengqi Gao, Sucheng Ren, Hang Zhao:
The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation. - Juhan Bae, Michael R. Zhang, Michael Ruan, Eric Wang, So Hasegawa, Jimmy Ba, Roger Baker Grosse:
Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve. - Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic:
Near-optimal Policy Identification in Active Reinforcement Learning. - Xiangzhe Kong, Wenbing Huang, Yang Liu:
Conditional Antibody Design as 3D Equivariant Graph Translation. - Kenneth Li, Aspen K. Hopkins, David Bau, Fernanda B. Viégas, Hanspeter Pfister, Martin Wattenberg:
Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task. - Haozhe Ji, Pei Ke, Zhipeng Hu, Rongsheng Zhang, Minlie Huang:
Tailoring Language Generation Models under Total Variation Distance. - Vincent Micheli, Eloi Alonso, François Fleuret:
Transformers are Sample-Efficient World Models. - Frederic Koehler, Alexander Heckett, Andrej Risteski:
Statistical Efficiency of Score Matching: The View from Isoperimetry. - Yiming Zuo, Jia Deng:
View Synthesis with Sculpted Neural Points. - Zizhao Zhang, Xin Wang, Chaoyu Guan, Ziwei Zhang, Haoyang Li, Wenwu Zhu:
AutoGT: Automated Graph Transformer Architecture Search. - Yunhao Zhang, Junchi Yan:
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting. - Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric P. Xing:
Betty: An Automatic Differentiation Library for Multilevel Optimization. - Haoran Xu, Li Jiang, Jianxiong Li, Zhuoran Yang, Zhaoran Wang, Wai Kin Victor Chan, Xianyuan Zhan:
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization. - Pan Zhou, Xingyu Xie, Shuicheng Yan:
Win: Weight-Decay-Integrated Nesterov Acceleration for Adaptive Gradient Algorithms. - Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, Mingkui Tan:
Towards Stable Test-time Adaptation in Dynamic Wild World. - Jingtao Li, Lingjuan Lyu, Daisuke Iso, Chaitali Chakrabarti, Michael Spranger:
MocoSFL: enabling cross-client collaborative self-supervised learning. - Siwei Chen, Yiqing Xu, Cunjun Yu, Linfeng Li, Xiao Ma, Zhongwen Xu, David Hsu:
DaxBench: Benchmarking Deformable Object Manipulation with Differentiable Physics. - Ivan Skorokhodov, Aliaksandr Siarohin, Yinghao Xu, Jian Ren, Hsin-Ying Lee, Peter Wonka, Sergey Tulyakov:
3D generation on ImageNet. - Bohang Zhang, Shengjie Luo, Liwei Wang, Di He:
Rethinking the Expressive Power of GNNs via Graph Biconnectivity. - Bo Li, Yifei Shen, Jingkang Yang, Yezhen Wang, Jiawei Ren, Tong Che, Jun Zhang, Ziwei Liu:
Sparse Mixture-of-Experts are Domain Generalizable Learners. - Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, Judy Hoffman:
Token Merging: Your ViT But Faster. - Jiajun Fan, Yuzheng Zhuang, Yuecheng Liu, Jianye Hao, Bin Wang, Jiangcheng Zhu, Hao Wang, Shu-Tao Xia:
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection. - Xu Ma, Yuqian Zhou, Huan Wang, Can Qin, Bin Sun, Chang Liu, Yun Fu:
Image as Set of Points.
Notable-top-25%
- Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin T. Vechev:
Human-Guided Fair Classification for Natural Language Processing. - Qiongkai Xu, Christian Walder, Chenchen Xu:
Humanly Certifying Superhuman Classifiers. - Jayaram Raghuram, Yijing Zeng, Dolores García, Rafael Ruiz, Somesh Jha, Joerg Widmer, Suman Banerjee:
Few-Shot Domain Adaptation For End-to-End Communication. - Liyao Li, Haobo Wang, Liangyu Zha, Qingyi Huang, Sai Wu, Gang Chen, Junbo Zhao:
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering. - Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt:
Learning Group Importance using the Differentiable Hypergeometric Distribution. - Andrea Bontempelli, Stefano Teso, Katya Tentori, Fausto Giunchiglia, Andrea Passerini:
Concept-level Debugging of Part-Prototype Networks. - Félix Chalumeau, Raphaël Boige, Bryan Lim, Valentin Macé, Maxime Allard, Arthur Flajolet, Antoine Cully, Thomas Pierrot:
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery. - Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu:
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. - Zhenghai Xue, Zhenghao Peng, Quanyi Li, Zhihan Liu, Bolei Zhou:
Guarded Policy Optimization with Imperfect Online Demonstrations. - Zenan Li, Zehua Liu, Yuan Yao, Jingwei Xu, Taolue Chen, Xiaoxing Ma, Jian Lü:
Learning with Logical Constraints but without Shortcut Satisfaction. - Mark Niklas Müller, Franziska Eckert, Marc Fischer, Martin T. Vechev:
Certified Training: Small Boxes are All You Need. - Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Lihong Gu, Xiaodong Zeng, Wenwu Zhu:
Multi-Objective Online Learning. - Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou:
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning. - Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. - Kuno Kim, Stefano Ermon:
Understanding and Adopting Rational Behavior by Bellman Score Estimation. - Jaehyun Nam, Jihoon Tack, Kyungmin Lee, Hankook Lee, Jinwoo Shin:
STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables. - Simran Arora, Avanika Narayan, Mayee F. Chen, Laurel J. Orr, Neel Guha, Kush Bhatia, Ines Chami, Christopher Ré:
Ask Me Anything: A simple strategy for prompting language models. - Ziang Chen, Jialin Liu, Xinshang Wang, Wotao Yin:
On Representing Linear Programs by Graph Neural Networks. - Sungyub Kim, Sihwan Park, Kyung-Su Kim, Eunho Yang:
Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel. - Yubei Chen, Zeyu Yun, Yi Ma, Bruno A. Olshausen, Yann LeCun:
Minimalistic Unsupervised Representation Learning with the Sparse Manifold Transform. - Mingze Dong, Yuval Kluger:
GEASS: Neural causal feature selection for high-dimensional biological data. - Sheng Li, Geng Yuan, Yue Dai, Youtao Zhang, Yanzhi Wang, Xulong Tang:
SmartFRZ: An Efficient Training Framework using Attention-Based Layer Freezing. - Chenjun Xiao, Han Wang, Yangchen Pan, Adam White, Martha White:
The In-Sample Softmax for Offline Reinforcement Learning. - Huiwon Jang, Hankook Lee, Jinwoo Shin:
Unsupervised Meta-learning via Few-shot Pseudo-supervised Contrastive Learning. - Hankook Lee, Jongheon Jeong, Sejun Park, Jinwoo Shin:
Guiding Energy-based Models via Contrastive Latent Variables. - Avrajit Ghosh, He Lyu, Xitong Zhang, Rongrong Wang:
Implicit regularization in Heavy-ball momentum accelerated stochastic gradient descent. - Matthew Dowling, Yuan Zhao, Il Memming Park:
Real-time variational method for learning neural trajectory and its dynamics. - Ze Wang, Jiang Wang, Zicheng Liu, Qiang Qiu:
Energy-Inspired Self-Supervised Pretraining for Vision Models. - Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, Tao Yu:
Binding Language Models in Symbolic Languages. - Zhong Yi Wan, Leonardo Zepeda-Núñez, Anudhyan Boral, Fei Sha:
Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems. - Andrew Szot, Amy Zhang, Dhruv Batra, Zsolt Kira, Franziska Meier:
BC-IRL: Learning Generalizable Reward Functions from Demonstrations. - Matthew Ricci, Noa Moriel, Zoe Piran, Mor Nitzan:
Phase2vec: dynamical systems embedding with a physics-informed convolutional network. - Qinsheng Zhang, Molei Tao, Yongxin Chen:
gDDIM: Generalized denoising diffusion implicit models.