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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 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. - Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi:
FedExP: Speeding Up Federated Averaging via Extrapolation. - Si Si, Felix X. Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar:
Serving Graph Compression for Graph Neural Networks. - Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla:
Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency. - Yuan Gong, Andrew Rouditchenko, Alexander H. Liu, David Harwath, Leonid Karlinsky, Hilde Kuehne, James R. Glass:
Contrastive Audio-Visual Masked Autoencoder. - Daniel Kunin, Atsushi Yamamura, Chao Ma, Surya Ganguli:
The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks. - Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette, Marc Peter Deisenroth:
Optimal Transport for Offline Imitation Learning. - Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, Yejin Choi:
Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization. - Zahra Kadkhodaie, Florentin Guth, Stéphane Mallat, Eero P. Simoncelli:
Learning multi-scale local conditional probability models of images. - James C. R. Whittington, Will Dorrell, Surya Ganguli, Timothy Behrens:
Disentanglement with Biological Constraints: A Theory of Functional Cell Types. - Kelsey R. Allen, Yulia Rubanova, Tatiana Lopez-Guevara, William Whitney, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Tobias Pfaff:
Learning rigid dynamics with face interaction graph networks. - Arthur Jacot:
Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions. - Yunwei Ren, Mo Zhou, Rong Ge:
Depth Separation with Multilayer Mean-Field Networks. - Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski:
Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. - Lorenz Kuhn, Yarin Gal, Sebastian Farquhar:
Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation. - Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten:
DINO as a von Mises-Fisher mixture model. - Uday Kamal, Saurabh Dash, Saibal Mukhopadhyay:
Associative Memory Augmented Asynchronous Spatiotemporal Representation Learning for Event-based Perception. - Yanchao Sun, Shuang Ma, Ratnesh Madaan, Rogerio Bonatti, Furong Huang, Ashish Kapoor:
SMART: Self-supervised Multi-task pretrAining with contRol Transformers. - Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, Joseph E. Gonzalez:
TEMPERA: Test-Time Prompt Editing via Reinforcement Learning. - Rem Yang, Jacob Laurel, Sasa Misailovic, Gagandeep Singh:
Provable Defense Against Geometric Transformations. - Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson:
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. - Eoin M. Kenny, Mycal Tucker, Julie Shah:
Towards Interpretable Deep Reinforcement Learning with Human-Friendly Prototypes. - Dian Wang, Jung Yeon Park, Neel Sortur, Lawson L. S. Wong, Robin Walters, Robert Platt:
The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry. - Zhili Liu, Kai Chen, Jianhua Han, Lanqing Hong, Hang Xu, Zhenguo Li, James T. Kwok:
Task-customized Masked Autoencoder via Mixture of Cluster-conditional Experts. - Jivat Neet Kaur, Emre Kiciman, Amit Sharma:
Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. - Lisa Dunlap, Clara Mohri, Devin Guillory, Han Zhang, Trevor Darrell, Joseph E. Gonzalez, Aditi Raghunathan, Anna Rohrbach:
Using Language to Extend to Unseen Domains. - Zhuoqing Song, Jason D. Lee, Zhuoran Yang:
Can We Find Nash Equilibria at a Linear Rate in Markov Games? - Adrien Journé, Hector Garcia Rodriguez, Qinghai Guo, Timoleon Moraitis:
Hebbian Deep Learning Without Feedback. - Edoardo Balzani, Jean-Paul Noel, Pedro Herrero-Vidal, Dora E. Angelaki, Cristina Savin:
A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation. - Shoaib Ahmed Siddiqui, Nitarshan Rajkumar, Tegan Maharaj, David Krueger, Sara Hooker:
Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics. - Quentin Bouniot, Romaric Audigier, Angélique Loesch, Amaury Habrard:
Proposal-Contrastive Pretraining for Object Detection from Fewer Data. - Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim:
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations. - Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries. - Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Alexandre Lacoste, Sai Rajeswar:
Choreographer: Learning and Adapting Skills in Imagination. - Jake Bruce, Ankit Anand, Bogdan Mazoure, Rob Fergus:
Learning About Progress From Experts. - Hongyi Ling, Zhimeng Jiang, Youzhi Luo, Shuiwang Ji, Na Zou:
Learning Fair Graph Representations via Automated Data Augmentations. - Erik Wijmans, Manolis Savva, Irfan Essa, Stefan Lee, Ari S. Morcos, Dhruv Batra:
Emergence of Maps in the Memories of Blind Navigation Agents. - Lu Lin, Jinghui Chen, Hongning Wang:
Spectral Augmentation for Self-Supervised Learning on Graphs. - Thanh Nguyen-Tang, Raman Arora:
VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation. - Léon Zheng, Gilles Puy, Elisa Riccietti, Patrick Pérez, Rémi Gribonval:
Self-supervised learning with rotation-invariant kernels. - Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams:
Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity. - Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, Amy Zhang:
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training. - Ainesh Bakshi, Piotr Indyk, Praneeth Kacham, Sandeep Silwal, Samson Zhou:
Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation. - Pankaj K. Agarwal, Sharath Raghvendra, Pouyan Shirzadian, Rachita Sowle:
A Higher Precision Algorithm for Computing the $1$-Wasserstein Distance. - Sylvestre-Alvise Rebuffi, Francesco Croce, Sven Gowal:
Revisiting adapters with adversarial training. - Zhenting Wang, Kai Mei, Juan Zhai, Shiqing Ma:
UNICORN: A Unified Backdoor Trigger Inversion Framework. - Aleksandar Pavlovic, Emanuel Sallinger:
ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion. - Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann:
Localized Randomized Smoothing for Collective Robustness Certification. - Xiaoling Hu, Dimitris Samaras, Chao Chen:
Learning Probabilistic Topological Representations Using Discrete Morse Theory. - Scott Sussex, Anastasia Makarova, Andreas Krause:
Model-based Causal Bayesian Optimization. - Khai Loong Aw, Mariya Toneva:
Training language models to summarize narratives improves brain alignment. - Danilo Numeroso, Davide Bacciu, Petar Velickovic:
Dual Algorithmic Reasoning. - Tan Minh Nguyen, Tam Minh Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
A Primal-Dual Framework for Transformers and Neural Networks. - Jezabel R. Garcia, Federica Freddi, Stathi Fotiadis, Maolin Li, Sattar Vakili, Alberto Bernacchia, Guillaume Hennequin:
Fisher-Legendre (FishLeg) optimization of deep neural networks. - Sen Yang, Wen Heng, Gang Liu, Guozhong Luo, Wankou Yang, Gang Yu:
Capturing the Motion of Every Joint: 3D Human Pose and Shape Estimation with Independent Tokens. - Anand Subramoney, Khaleelulla Khan Nazeer, Mark Schöne, Christian Mayr, David Kappel:
Efficient recurrent architectures through activity sparsity and sparse back-propagation through time. - Xingchao Liu, Chengyue Gong, Qiang Liu:
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. - Ranjie Duan, Yuefeng Chen, Yao Zhu, Xiaojun Jia, Rong Zhang, Hui Xue:
Inequality phenomenon in l∞-adversarial training, and its unrealized threats. - Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu:
Learning Diffusion Bridges on Constrained Domains. - Andrii Zadaianchuk, Matthäus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox:
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. - Hao He, Kaiwen Zha, Dina Katabi:
Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning. - Congyu Qiao, Ning Xu, Xin Geng:
Decompositional Generation Process for Instance-Dependent Partial Label Learning. - Jean-Baptiste Gaya, Thang Doan, Lucas Caccia, Laure Soulier, Ludovic Denoyer, Roberta Raileanu:
Building a Subspace of Policies for Scalable Continual Learning. - Jing Zhou, Zongyu Lin, Yanan Zheng, Jian Li, Zhilin Yang:
Not All Tasks Are Born Equal: Understanding Zero-Shot Generalization. - Tong Yang, Michael I. Jordan, Tatjana Chavdarova:
Solving Constrained Variational Inequalities via a First-order Interior Point-based Method. - Xinbiao Wang, Junyu Liu, Tongliang Liu, Yong Luo, Yuxuan Du, Dacheng Tao:
Symmetric Pruning in Quantum Neural Networks. - Brian Chmiel, Itay Hubara, Ron Banner, Daniel Soudry:
Minimum Variance Unbiased N: M Sparsity for the Neural Gradients. - Xiaoman Pan, Wenlin Yao, Hongming Zhang, Dian Yu, Dong Yu, Jianshu Chen:
Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models. - Zhaoqing Wang, Ziyu Chen, Yaqian Li, Yandong Guo, Jun Yu, Mingming Gong, Tongliang Liu:
Mosaic Representation Learning for Self-supervised Visual Pre-training. - Zhou Xian, Bo Zhu, Zhenjia Xu, Hsiao-Yu Tung, Antonio Torralba, Katerina Fragkiadaki, Chuang Gan:
FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation. - Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matthew Le:
Flow Matching for Generative Modeling. - Xuan Li, Yi-Ling Qiao, Peter Yichen Chen, Krishna Murthy Jatavallabhula, Ming C. Lin, Chenfanfu Jiang, Chuang Gan:
PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification. - Tuomas P. Oikarinen, Tsui-Wei Weng:
CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks. - Eric Qu, Xufang Luo, Dongsheng Li:
Data Continuity Matters: Improving Sequence Modeling with Lipschitz Regularizer. - Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong:
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis. - Olga Golovneva, Moya Chen, Spencer Poff, Martin Corredor, Luke Zettlemoyer, Maryam Fazel-Zarandi, Asli Celikyilmaz:
ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning. - Peiyu Yang, Naveed Akhtar, Zeyi Wen, Mubarak Shah, Ajmal Saeed Mian:
Re-calibrating Feature Attributions for Model Interpretation. - Brandon Cui, Andrei Lupu, Samuel Sokota, Hengyuan Hu, David J. Wu, Jakob Nicolaus Foerster:
Adversarial Diversity in Hanabi. - Paria Rashidinejad, Hanlin Zhu, Kunhe Yang, Stuart Russell, Jiantao Jiao:
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian. - Shuyan Zhou, Uri Alon, Frank F. Xu, Zhengbao Jiang, Graham Neubig:
DocPrompting: Generating Code by Retrieving the Docs. - Hiroki Furuta, Yusuke Iwasawa, Yutaka Matsuo, Shixiang Shane Gu:
A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation. - Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, Jacob Steinhardt:
Progress measures for grokking via mechanistic interpretability. - Zhangyang Gao, Cheng Tan, Stan Z. Li:
PiFold: Toward effective and efficient protein inverse folding. - Edward S. Hu, Richard Chang, Oleh Rybkin, Dinesh Jayaraman:
Planning Goals for Exploration. - Yijie Wang, Yuan Zhou, Xiaoqing Huang, Kun Huang, Jie Zhang, Jianzhu Ma:
Learning Sparse Group Models Through Boolean Relaxation. - Zhen Liu, Yao Feng, Michael J. Black, Derek Nowrouzezahrai, Liam Paull, Weiyang Liu:
MeshDiffusion: Score-based Generative 3D Mesh Modeling. - Fan Chen, Yu Bai, Song Mei:
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms. - Hyungu Kahng, Hyungrok Do, Judy Zhong:
Domain Generalization via Heckman-type Selection Models. - Vanshaj Khattar, Yuhao Ding, Bilgehan Sel, Javad Lavaei, Ming Jin:
A CMDP-within-online framework for Meta-Safe Reinforcement Learning. - Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath:
Effects of Graph Convolutions in Multi-layer Networks. - Mert Yüksekgönül, Maggie Wang, James Zou:
Post-hoc Concept Bottleneck Models. - Li Yi, Gezheng Xu, Pengcheng Xu, Jiaqi Li, Ruizhi Pu, Charles Ling, A. Ian McLeod, Boyu Wang:
When Source-Free Domain Adaptation Meets Learning with Noisy Labels. - Alireza Mousavi Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu:
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. - Ahmed Touati, Jérémy Rapin, Yann Ollivier:
Does Zero-Shot Reinforcement Learning Exist? - Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt:
Hyperbolic Deep Reinforcement Learning. - Tailin Wu, Takashi Maruyama, Qingqing Zhao, Gordon Wetzstein, Jure Leskovec:
Learning Controllable Adaptive Simulation for Multi-resolution Physics. - John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi, Michael G. Rabbat:
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning. - Josua Sassen, Klaus Hildebrandt, Martin Rumpf, Benedikt Wirth:
Parametrizing Product Shape Manifolds by Composite Networks. - Rui Wen, Zhengyu Zhao, Zhuoran Liu, Michael Backes, Tianhao Wang, Yang Zhang:
Is Adversarial Training Really a Silver Bullet for Mitigating Data Poisoning? - Carles Domingo-Enrich, Yair Schiff, Youssef Mroueh:
Learning with Stochastic Orders. - Yongshuo Zong, Yongxin Yang, Timothy M. Hospedales:
MEDFAIR: Benchmarking Fairness for Medical Imaging. - Aldo Pacchiano, Drausin Wulsin, Robert A. Barton, Luis F. Voloch:
Neural Design for Genetic Perturbation Experiments. - Ronak Mehta, Jeffery Kline, Vishnu Suresh Lokhande, Glenn Fung, Vikas Singh:
Efficient Discrete Multi Marginal Optimal Transport Regularization. - Mansheej Paul, Feng Chen, Brett W. Larsen, Jonathan Frankle, Surya Ganguli, Gintare Karolina Dziugaite:
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? - Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang:
Quantifying Memorization Across Neural Language Models. - Kevin Frans, Phillip Isola:
Powderworld: A Platform for Understanding Generalization via Rich Task Distributions. - Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu:
Out-of-Distribution Detection and Selective Generation for Conditional Language Models. - Jeremiah Blocki, Seunghoon Lee, Tamalika Mukherjee, Samson Zhou:
Differentially Private $L_2$-Heavy Hitters in the Sliding Window Model. - Ruiqi Ni, Ahmed H. Qureshi:
NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning. - Guihong Li, Yuedong Yang, Kartikeya Bhardwaj, Radu Marculescu:
ZiCo: Zero-shot NAS via inverse Coefficient of Variation on Gradients. - Onno Eberhard, Jakob J. Hollenstein, Cristina Pinneri, Georg Martius:
Pink Noise Is All You Need: Colored Noise Exploration in Deep Reinforcement Learning. - Jayoung Kim, Chaejeong Lee, Noseong Park:
STaSy: Score-based Tabular data Synthesis. - Alexandre Araujo, Aaron J. Havens, Blaise Delattre, Alexandre Allauzen, Bin Hu:
A Unified Algebraic Perspective on Lipschitz Neural Networks. - Blake Bordelon, Cengiz Pehlevan:
The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks. - Arnab Kumar Mondal, Piyush Tiwary, Parag Singla, Prathosh AP:
Few-shot Cross-domain Image Generation via Inference-time Latent-code Learning. - Yiqin Tan, Pihe Hu, Ling Pan, Jiatai Huang, Longbo Huang:
RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch. - Shiwei Liu, Tianlong Chen, Zhenyu Zhang, Xuxi Chen, Tianjin Huang, Ajay Kumar Jaiswal, Zhangyang Wang:
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! - Tianlong Chen, Zhenyu Zhang, Ajay Kumar Jaiswal, Shiwei Liu, Zhangyang Wang:
Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers. - Zhiyuan Cheng, James Liang, Guanhong Tao, Dongfang Liu, Xiangyu Zhang:
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks. - Tianlin Liu, Joan Puigcerver, Mathieu Blondel:
Sparsity-Constrained Optimal Transport. - Shuyang Yu, Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou:
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection. - Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, Junchi Yan:
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion. - Takeshi Koshizuka, Issei Sato:
Neural Lagrangian Schrödinger Bridge: Diffusion Modeling for Population Dynamics. - Ping-yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, Tom Goldstein:
Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent. - Jiahui Gao, Renjie Pi, Yong Lin, Hang Xu, Jiacheng Ye, Zhiyong Wu, Weizhong Zhang, Xiaodan Liang, Zhenguo Li, Lingpeng Kong:
Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning. - Tianbo Li, Min Lin, Zheyuan Hu, Kunhao Zheng, Giovanni Vignale, Kenji Kawaguchi, A. H. Castro Neto, Kostya S. Novoselov, Shuicheng Yan:
D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory. - Do-Yeon Kim, Dong-Jun Han, Jun Seo, Jaekyun Moon:
Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning. - Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin:
Pre-training via Denoising for Molecular Property Prediction. - Hyungi Lee, Eunggu Yun, Giung Nam, Edwin Fong, Juho Lee:
Martingale Posterior Neural Processes. - Tiago Pimentel, Clara Meister, Ryan Cotterell:
On the Usefulness of Embeddings, Clusters and Strings for Text Generation Evaluation. - Pierre Schumacher, Daniel F. B. Haeufle, Dieter Büchler, Syn Schmitt, Georg Martius:
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems. - Ian Gemp, Charlie Chen, Brian McWilliams:
The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium. - Shuguang Dou, Xinyang Jiang, Cairong Zhao, Dongsheng Li:
EA-HAS-Bench: Energy-aware Hyperparameter and Architecture Search Benchmark. - Krunoslav Lehman Pavasovic, Jonas Rothfuss, Andreas Krause:
MARS: Meta-learning as Score Matching in the Function Space. - Hualin Zhang, Bin Gu:
Faster Gradient-Free Methods for Escaping Saddle Points. - Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Luc Van Gool:
VA-DepthNet: A Variational Approach to Single Image Depth Prediction. - Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, Daniel Cohen-Or:
Prompt-to-Prompt Image Editing with Cross-Attention Control. - Guillaume Couairon, Jakob Verbeek, Holger Schwenk, Matthieu Cord:
DiffEdit: Diffusion-based semantic image editing with mask guidance. - Jiyeon Han, Hwanil Choi, Yunjey Choi, Junho Kim, Jung-Woo Ha, Jaesik Choi:
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images. - Yuxin Fang, Li Dong, Hangbo Bao, Xinggang Wang, Furu Wei:
Corrupted Image Modeling for Self-Supervised Visual Pre-Training. - Longlin Yu, Cheng Zhang:
Semi-Implicit Variational Inference via Score Matching. - Taeoh Kim, Jinhyung Kim, Minho Shim, Sangdoo Yun, Myunggu Kang, Dongyoon Wee, Sangyoun Lee:
Exploring Temporally Dynamic Data Augmentation for Video Recognition. - Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan:
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. - Yuzhe Ma, Zhijin Zhou:
Adversarial Attacks on Adversarial Bandits. - Tianyu Zhao, Xiang Pan, Minghua Chen, Steven H. Low:
Ensuring DNN Solution Feasibility for Optimization Problems with Linear Constraints. - Xuheng Cai, Chao Huang, Lianghao Xia, Xubin Ren:
LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. - Jinxi Xiang, Kuan Tian, Jun Zhang:
MIMT: Masked Image Modeling Transformer for Video Compression. - Daniel Y. Fu, Tri Dao, Khaled Kamal Saab, Armin W. Thomas, Atri Rudra, Christopher Ré:
Hungry Hungry Hippos: Towards Language Modeling with State Space Models. - Yuelin Wang, Kai Yi, Xinliang Liu, Yu Guang Wang, Shi Jin:
ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks. - Cameron Diao, Ricky Loynd:
Relational Attention: Generalizing Transformers for Graph-Structured Tasks. - Saachi Jain, Hannah Lawrence, Ankur Moitra, Aleksander Madry:
Distilling Model Failures as Directions in Latent Space. - Shuting Shen, Junwei Lu:
Combinatorial-Probabilistic Trade-Off: P-Values of Community Properties Test in the Stochastic Block Models. - Jun-Kun Wang, Andre Wibisono:
Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization. - Ashish Gaurav, Kasra Rezaee, Guiliang Liu, Pascal Poupart:
Learning Soft Constraints From Constrained Expert Demonstrations. - Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen, Philip Greengard, Leonid Karlinsky, Rogério Feris, David Daniel Cox, Zhangyang Wang, Yoon Kim:
Learning to Grow Pretrained Models for Efficient Transformer Training. - Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, Mike Lewis:
InCoder: A Generative Model for Code Infilling and Synthesis. - Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, Aniruddha Kembhavi:
UNIFIED-IO: A Unified Model for Vision, Language, and Multi-modal Tasks. - Nico Gürtler, Sebastian Blaes, Pavel Kolev, Felix Widmaier, Manuel Wuthrich, Stefan Bauer, Bernhard Schölkopf, Georg Martius:
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware. - Sumyeong Ahn, Jongwoo Ko, Se-Young Yun:
CUDA: Curriculum of Data Augmentation for Long-tailed Recognition. - Ian Connick Covert, Chanwoo Kim, Su-In Lee:
Learning to Estimate Shapley Values with Vision Transformers. - Ido Galil, Mohammed Dabbah, Ran El-Yaniv:
A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet. - Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao, Richard G. Baraniuk, Anima Anandkumar:
Retrieval-based Controllable Molecule Generation. - Sirui Xu, Yu-Xiong Wang, Liangyan Gui:
Stochastic Multi-Person 3D Motion Forecasting. - Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka:
Sign and Basis Invariant Networks for Spectral Graph Representation Learning. - Xiajun Jiang, Ryan Missel, Zhiyuan Li, Linwei Wang:
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting. - Marc Szafraniec, Baptiste Rozière, Hugh Leather, Patrick Labatut, François Charton, Gabriel Synnaeve:
Code Translation with Compiler Representations. - Ziming Liu, Eric J. Michaud, Max Tegmark:
Omnigrok: Grokking Beyond Algorithmic Data. - Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Flow Annealed Importance Sampling Bootstrap. - Zhiyuan Li, Xiajun Jiang, Ryan Missel, Prashnna Kumar Gyawali, Nilesh Kumar, Linwei Wang:
Continual Unsupervised Disentangling of Self-Organizing Representations. - Zhihao Shi, Xize Liang, Jie Wang:
LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence. - Renhao Wang, Jiayuan Mao, Joy Hsu, Hang Zhao, Jiajun Wu, Yang Gao:
Programmatically Grounded, Compositionally Generalizable Robotic Manipulation. - Qiang Wang, Haoge Deng, Yonggang Qi, Da Li, Yi-Zhe Song:
SketchKnitter: Vectorized Sketch Generation with Diffusion Models. - Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, De-Chuan Zhan:
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning. - Zhen Qin, Xiaodong Han, Weixuan Sun, Bowen He, Dong Li, Dongxu Li, Yuchao Dai, Lingpeng Kong, Yiran Zhong:
Toeplitz Neural Network for Sequence Modeling. - Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk, Zorah Lähner, Michael Moeller, Vladislav Golyanik:
QuAnt: Quantum Annealing with Learnt Couplings. - Yiming Gao, Feiyu Liu, Liang Wang, Zhenjie Lian, Weixuan Wang, Siqin Li, Xianliang Wang, Xianhan Zeng, Rundong Wang, Jiawei Wang, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu:
Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective. - Jérôme Bolte, Ryan Boustany, Edouard Pauwels, Béatrice Pesquet-Popescu:
On the complexity of nonsmooth automatic differentiation. - Hyungjin Chung, Jeongsol Kim, Michael Thompson McCann, Marc Louis Klasky, Jong Chul Ye:
Diffusion Posterior Sampling for General Noisy Inverse Problems. - Kevin Meng, Arnab Sen Sharma, Alex J. Andonian, Yonatan Belinkov, David Bau:
Mass-Editing Memory in a Transformer. - Yi Li, Honghao Lin, Simin Liu, Ali Vakilian, David P. Woodruff:
Learning the Positions in CountSketch. - Daesol Cho, Seungjae Lee, H. Jin Kim:
Outcome-directed Reinforcement Learning by Uncertainty \& Temporal Distance-Aware Curriculum Goal Generation. - Yingda Yin, Yang Wang, He Wang, Baoquan Chen:
A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation. - Xiaosong Zhang, Yunjie Tian, Lingxi Xie, Wei Huang, Qi Dai, Qixiang Ye, Qi Tian:
HiViT: A Simpler and More Efficient Design of Hierarchical Vision Transformer. - Qing Li, Siyuan Huang, Yining Hong, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu:
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics. - Mononito Goswami, Cristian I. Challu, Laurent Callot, Lenon Minorics, Andrey Kan:
Unsupervised Model Selection for Time Series Anomaly Detection. - Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar:
AANG : Automating Auxiliary Learning. - Maor Ashkenazi, Zohar Rimon, Ron Vainshtein, Shir Levi, Elad Richardson, Pinchas Mintz, Eran Treister:
NeRN: Learning Neural Representations for Neural Networks. - Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever:
Formal Mathematics Statement Curriculum Learning. - Nimrod Berman, Ilan Naiman, Omri Azencot:
Multifactor Sequential Disentanglement via Structured Koopman Autoencoders. - Olivier Laurent, Adrien Lafage, Enzo Tartaglione, Geoffrey Daniel, Jean-Marc Martinez, Andrei Bursuc, Gianni Franchi:
Packed Ensembles for efficient uncertainty estimation. - Shaolei Zhang, Yang Feng:
Hidden Markov Transformer for Simultaneous Machine Translation. - Shaoan Xie, Lingjing Kong, Mingming Gong, Kun Zhang:
Multi-domain image generation and translation with identifiability guarantees. - Matthias De Lange, Gido M. van de Ven, Tinne Tuytelaars:
Continual evaluation for lifelong learning: Identifying the stability gap. - Zihao Xu, Guang-Yuan Hao, Hao He, Hao Wang:
Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation. - Shutong Wu, Sizhe Chen, Cihang Xie, Xiaolin Huang:
One-Pixel Shortcut: On the Learning Preference of Deep Neural Networks. - Gianluigi Silvestri, Daan Roos, Luca Ambrogioni:
Deterministic training of generative autoencoders using invertible layers. - Yong Lin, Renjie Pi, Weizhong Zhang, Xiaobo Xia, Jiahui Gao, Xiao Zhou, Tongliang Liu, Bo Han:
A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond. - Jae Oh Woo:
Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle. - Ming Shi, Yingbin Liang, Ness B. Shroff:
Near-Optimal Adversarial Reinforcement Learning with Switching Costs. - Chenhongyi Yang, Jiarui Xu, Shalini De Mello, Elliot J. Crowley, Xiaolong Wang:
GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation. - Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev:
Neural Optimal Transport. - Mixue Xie, Shuang Li, Rui Zhang, Chi Harold Liu:
Dirichlet-based Uncertainty Calibration for Active Domain Adaptation. - Jiale Zhang, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, Xin Yuan:
Accurate Image Restoration with Attention Retractable Transformer. - Zhuo Li, Derui Zhu, Yujing Hu, Xiaofei Xie, Lei Ma, Yan Zheng, Yan Song, Yingfeng Chen, Jianjun Zhao:
Neural Episodic Control with State Abstraction. - Tuomas Kynkäänniemi, Tero Karras, Miika Aittala, Timo Aila, Jaakko Lehtinen:
The Role of ImageNet Classes in Fréchet Inception Distance. - Mingi Kwon, Jaeseok Jeong, Youngjung Uh:
Diffusion Models Already Have A Semantic Latent Space. - Yinhuai Wang, Jiwen Yu, Jian Zhang:
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model. - Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra:
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities. - Deval Shah, Tor M. Aamodt:
Learning Label Encodings for Deep Regression. - Jiayuan Gu, Devendra Singh Chaplot, Hao Su, Jitendra Malik:
Multi-skill Mobile Manipulation for Object Rearrangement. - Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig:
Single-shot General Hyper-parameter Optimization for Federated Learning. - Samuel Lavoie, Christos Tsirigotis, Max Schwarzer, Ankit Vani, Michael Noukhovitch, Kenji Kawaguchi, Aaron C. Courville:
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification. - Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, Yu Qiao:
Vision Transformer Adapter for Dense Predictions. - Jianfei Yang, Xiangyu Peng, Kai Wang, Zheng Zhu, Jiashi Feng, Lihua Xie, Yang You:
Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors. - Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, Kun Zhang:
PLOT: Prompt Learning with Optimal Transport for Vision-Language Models. - Alexander Tyurin, Peter Richtárik:
DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity. - Hoang Anh Just, Feiyang Kang, Tianhao Wang, Yi Zeng, Myeongseob Ko, Ming Jin, Ruoxi Jia:
LAVA: Data Valuation without Pre-Specified Learning Algorithms. - Hayeon Lee, Sohyun An, Minseon Kim, Sung Ju Hwang:
Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets. - Yoni Choukroun, Lior Wolf:
Denoising Diffusion Error Correction Codes. - Yadan Luo, Zhuoxiao Chen, Zijian Wang, Xin Yu, Zi Huang, Mahsa Baktashmotlagh:
Exploring Active 3D Object Detection from a Generalization Perspective. - Yujie Lu, Weixi Feng, Wanrong Zhu, Wenda Xu, Xin Eric Wang, Miguel P. Eckstein, William Yang Wang:
Neuro-Symbolic Procedural Planning with Commonsense Prompting. - Ling Pan, Dinghuai Zhang, Aaron C. Courville, Longbo Huang, Yoshua Bengio:
Generative Augmented Flow Networks. - Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, Somesh Jha:
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning. - Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho, Yue Chang, G. A. Pershing, Henrique Teles Maia, Maurizio M. Chiaramonte, Kevin T. Carlberg, Eitan Grinspun:
CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations. - Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Marcin Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael S. Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, Pete Florence:
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language. - Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, Sujan Kumar Gonugondla, Hantian Ding, Varun Kumar, Nathan Fulton, Arash Farahani, Siddhartha Jain, Robert Giaquinto, Haifeng Qian, Murali Krishna Ramanathan, Ramesh Nallapati:
Multi-lingual Evaluation of Code Generation Models. - Mohammadsajad Abavisani, David Danks, Sergey M. Plis:
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints. - Yi-Lun Liao, Tess E. Smidt:
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. - Dacheng Li, Hongyi Wang, Rulin Shao, Han Guo, Eric P. Xing, Hao Zhang:
MPCFORMER: Fast, Performant and Provate Transformer Inference with MPC. - Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, Jesse C. Cresswell:
Disparate Impact in Differential Privacy from Gradient Misalignment. - Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter:
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. - Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, Amit Haim Bermano:
Human Motion Diffusion Model. - Wenguan Wang, Cheng Han, Tianfei Zhou, Dongfang Liu:
Visual Recognition with Deep Nearest Centroids. - Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, Patrick Gallinari:
Continuous PDE Dynamics Forecasting with Implicit Neural Representations. - Mert Bülent Sariyildiz, Yannis Kalantidis, Karteek Alahari, Diane Larlus:
No Reason for No Supervision: Improved Generalization in Supervised Models. - Fangzhou Hong, Zhaoxi Chen, Yushi Lan, Liang Pan, Ziwei Liu:
EVA3D: Compositional 3D Human Generation from 2D Image Collections. - Tong Wu, Jiaqi Wang, Xingang Pan, Xudong Xu, Christian Theobalt, Ziwei Liu, Dahua Lin:
Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction. - Rujikorn Charakorn, Poramate Manoonpong, Nat Dilokthanakul:
Generating Diverse Cooperative Agents by Learning Incompatible Policies. - Timo Schick, Jane A. Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, Sebastian Riedel:
PEER: A Collaborative Language Model. - Zhengzhe Liu, Peng Dai, Ruihui Li, Xiaojuan Qi, Chi-Wing Fu:
ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation. - Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Henghui Zhu, Rui Dong, Deguang Kong, Juliette Burger, Anjelica Ramos, Zhiheng Huang, William Yang Wang, George Karypis, Bing Xiang, Dan Roth:
STREET: A Multi-Task Structured Reasoning and Explanation Benchmark. - Yibo Yang, Haobo Yuan, Xiangtai Li, Zhouchen Lin, Philip H. S. Torr, Dacheng Tao:
Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning. - Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Chris Cundy, Marcus Hutter, Shane Legg, Joel Veness, Pedro A. Ortega:
Neural Networks and the Chomsky Hierarchy. - Lingshen He, Yuxuan Chen, Zhengyang Shen, Yibo Yang, Zhouchen Lin:
Neural ePDOs: Spatially Adaptive Equivariant Partial Differential Operator Based Networks. - Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit Haim Bermano, Gal Chechik, Daniel Cohen-Or:
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion. - Ruifei He, Shuyang Sun, Xin Yu, Chuhui Xue, Wenqing Zhang, Philip H. S. Torr, Song Bai, Xiaojuan Qi:
Is Synthetic Data from Generative Models Ready for Image Recognition? - Bencheng Liao, Shaoyu Chen, Xinggang Wang, Tianheng Cheng, Qian Zhang, Wenyu Liu, Chang Huang:
MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction. - Jikai Jin, Yiping Lu, José H. Blanchet, Lexing Ying:
Minimax Optimal Kernel Operator Learning via Multilevel Training. - Keyu Tian, Yi Jiang, Qishuai Diao, Chen Lin, Liwei Wang, Zehuan Yuan:
Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling.
Poster
- Julius Adebayo, Melissa Hall, Bowen Yu, Bobbie Chern:
Quantifying and Mitigating the Impact of Label Errors on Model Disparity Metrics. - Alasdair Tran, Alexander Patrick Mathews, Lexing Xie, Cheng Soon Ong:
Factorized Fourier Neural Operators. - Tanay Narshana, Chaitanya Murti, Chiranjib Bhattacharyya:
DFPC: Data flow driven pruning of coupled channels without data. - Chaitanya Murti, Tanay Narshana, Chiranjib Bhattacharyya:
TVSPrune - Pruning Non-discriminative filters via Total Variation separability of intermediate representations without fine tuning. - Fabian Latorre, Igor Krawczuk, Leello Tadesse Dadi, Thomas Pethick, Volkan Cevher:
Finding Actual Descent Directions for Adversarial Training. - Shuangshuang Chen, Sihao Ding, Yiannis Karayiannidis, Mårten Björkman:
Learning Continuous Normalizing Flows For Faster Convergence To Target Distribution via Ascent Regularizations. - Zenan Li, Yuan Yao, Taolue Chen, Jingwei Xu, Chun Cao, Xiaoxing Ma, Jian Lü:
Softened Symbol Grounding for Neuro-symbolic Systems. - Gregory Schwartzman:
Mini-batch k-means terminates within O(d/ϵ) iterations. - Yu Yu, Hassan Sajjad, Jia Xu:
Learning Uncertainty for Unknown Domains with Zero-Target-Assumption. - Wenqiang Li, Weijun Li, Linjun Sun, Min Wu, Lina Yu, Jingyi Liu, Yanjie Li, Songsong Tian:
Transformer-based model for symbolic regression via joint supervised learning. - Asaf Yehudai, Matan Vetzler, Yosi Mass, Koren Lazar, Doron Cohen, Boaz Carmeli:
QAID: Question Answering Inspired Few-shot Intent Detection. - Thomas Pethick, Olivier Fercoq, Puya Latafat, Panagiotis Patrinos, Volkan Cevher:
Solving stochastic weak Minty variational inequalities without increasing batch size. - Yuxing Wang, Shuang Wu, Haobo Fu, Qiang Fu, Tiantian Zhang, Yongzhe Chang, Xueqian Wang:
Curriculum-based Co-design of Morphology and Control of Voxel-based Soft Robots. - Tribhuvanesh Orekondy, Kumar Pratik, Shreya Kadambi, Hao Ye, Joseph Soriaga, Arash Behboodi:
WiNeRT: Towards Neural Ray Tracing for Wireless Channel Modelling and Differentiable Simulations. - Firas Al-Hafez, Davide Tateo, Oleg Arenz, Guoping Zhao, Jan Peters:
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning. - Zebang Shen, Jiayuan Ye, Anmin Kang, Hamed Hassani, Reza Shokri:
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning. - Alexandre Devillers, Mathieu Lefort:
EquiMod: An Equivariance Module to Improve Visual Instance Discrimination. - Prashant Shivaram Bhat, Bahram Zonooz, Elahe Arani:
Task-Aware Information Routing from Common Representation Space in Lifelong Learning. - Nadezhda Chirkova, Sergey Troshin:
CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code. - André Ferreira Cruz, Catarina G. Belém, João Bravo, Pedro Saleiro, Pedro Bizarro:
FairGBM: Gradient Boosting with Fairness Constraints. - Aristotelis Chrysakis, Marie-Francine Moens:
Online Bias Correction for Task-Free Continual Learning. - Hugo Schmutz, Olivier Humbert, Pierre-Alexandre Mattei:
Don't fear the unlabelled: safe semi-supervised learning via debiasing. - Qizhang Li, Yiwen Guo, Wangmeng Zuo, Hao Chen:
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples. - Yanwen Fang, Yuxi Cai, Jintai Chen, Jingyu Zhao, Guangjian Tian, Guodong Li:
Cross-Layer Retrospective Retrieving via Layer Attention. - Shmuel Bar-David, Itamar Zimerman, Eliya Nachmani, Lior Wolf:
Decision S4: Efficient Sequence-Based RL via State Spaces Layers. - Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie:
Unveiling the sampling density in non-uniform geometric graphs. - An Zhang, Fangfu Liu, Wenchang Ma, Zhibo Cai, Xiang Wang, Tat-Seng Chua:
Boosting Causal Discovery via Adaptive Sample Reweighting. - Matthias Cosler, Frederik Schmitt, Christopher Hahn, Bernd Finkbeiner:
Iterative Circuit Repair Against Formal Specifications. - Mingxu Tao, Yansong Feng, Dongyan Zhao:
Can BERT Refrain from Forgetting on Sequential Tasks? A Probing Study. - Zifeng Zhuang, Kun Lei, Jinxin Liu, Donglin Wang, Yilang Guo:
Behavior Proximal Policy Optimization. - Will Dorrell, Peter E. Latham, Tim E. J. Behrens, James C. R. Whittington:
Actionable Neural Representations: Grid Cells from Minimal Constraints. - Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, Stan Z. Li:
Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules. - Cheongjae Jang, Yonghyeon Lee, Yung-Kyun Noh, Frank C. Park:
Geometrically regularized autoencoders for non-Euclidean data. - Yifei Wang, Qi Zhang, Tianqi Du, Jiansheng Yang, Zhouchen Lin, Yisen Wang:
A Message Passing Perspective on Learning Dynamics of Contrastive Learning. - Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet, Bryan Kian Hsiang Low:
Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation. - Taiji Suzuki, Atsushi Nitanda, Denny Wu:
Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics. - Yang Jiao, Kai Yang, Tiancheng Wu, Dongjin Song, Chengtao Jian:
Asynchronous Distributed Bilevel Optimization. - Daeho Um, Jiwoong Park, Seulki Park, Jin Young Choi:
Confidence-Based Feature Imputation for Graphs with Partially Known Features. - Ziwei Chen, Qiang Li, Xiaofeng Wang, Wankou Yang:
LiftedCL: Lifting Contrastive Learning for Human-Centric Perception. - Antti Koskela, Marlon Tobaben, Antti Honkela:
Individual Privacy Accounting with Gaussian Differential Privacy. - Thomas Pierrot, Arthur Flajolet:
Evolving Populations of Diverse RL Agents with MAP-Elites. - Gresa Shala, André Biedenkapp, Frank Hutter, Josif Grabocka:
Gray-Box Gaussian Processes for Automated Reinforcement Learning. - Chence Shi, Chuanrui Wang, Jiarui Lu, Bozitao Zhong, Jian Tang:
Protein Sequence and Structure Co-Design with Equivariant Translation. - Matt Jones, Tyler R. Scott, Mengye Ren, Gamaleldin Fathy Elsayed, Katherine L. Hermann, David Mayo, Michael Curtis Mozer:
Learning in temporally structured environments. - Laurent Condat, Peter Richtárik:
RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates. - Guande He, Jianfei Chen, Jun Zhu:
Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models. - Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano, Alexander M. Bronstein:
Fast Nonlinear Vector Quantile Regression. - Joshua Robinson, David Wingate:
Leveraging Large Language Models for Multiple Choice Question Answering. - Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang:
Regression with Label Differential Privacy. - Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang:
Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. - Yuning Cui, Yi Tao, Zhenshan Bing, Wenqi Ren, Xinwei Gao, Xiaochun Cao, Kai Huang, Alois Knoll:
Selective Frequency Network for Image Restoration. - Parth Sheth, Pengtao Xie:
Improving Differentiable Neural Architecture Search by Encouraging Transferability. - Neo Wei Ming, Zhehui Wang, Cheng Liu, Rick Siow Mong Goh, Tao Luo:
MA-BERT: Towards Matrix Arithmetic-only BERT Inference by Eliminating Complex Non-Linear Functions. - Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Efficient Certified Training and Robustness Verification of Neural ODEs. - Yu Liu, Mingbo Zhao, Zhao Zhang, Jicong Fan, Yang Lou, Shuicheng Yan:
Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing. - Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Denny Zhou, Neil Houlsby, Donald Metzler:
UL2: Unifying Language Learning Paradigms. - Hongwei Han, Mengyu Zhou, Shi Han, Xiu Li, Dongmei Zhang:
CASR: Generating Complex Sequences with Autoregressive Self-Boost Refinement. - Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine:
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. - Matus Telgarsky:
Feature selection and low test error in shallow low-rotation ReLU networks. - Subham Sekhar Sahoo, Anselm Paulus, Marin Vlastelica, Vít Musil, Volodymyr Kuleshov, Georg Martius:
Backpropagation through Combinatorial Algorithms: Identity with Projection Works. - Xiongye Xiao, Defu Cao, Ruochen Yang, Gaurav Gupta, Gengshuo Liu, Chenzhong Yin, Radu Balan, Paul Bogdan:
Coupled Multiwavelet Operator Learning for Coupled Differential Equations. - Michael Maynord, Eadom Dessalene, Cornelia Fermüller, Yiannis Aloimonos:
Mid-Vision Feedback. - Yannick Hogewind, Thiago D. Simão, Tal Kachman, Nils Jansen:
Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation. - Qian Lou, Yepeng Liu, Bo Feng:
TrojText: Test-time Invisible Textual Trojan Insertion. - Akarsh Pokkunuru, Pedram Rooshenas, Thilo Strauss, Anuj Abhishek, Taufiquar Khan:
Improved Training of Physics-Informed Neural Networks Using Energy-Based Priors: a Study on Electrical Impedance Tomography. - Yunchong Song, Chenghu Zhou, Xinbing Wang, Zhouhan Lin:
Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing. - Trenton Bricken, Xander Davies, Deepak Singh, Dmitry Krotov, Gabriel Kreiman:
Sparse Distributed Memory is a Continual Learner. - Kaiyuan Zhang, Guanhong Tao, Qiuling Xu, Siyuan Cheng, Shengwei An, Yingqi Liu, Shiwei Feng, Guangyu Shen, Pin-Yu Chen, Shiqing Ma, Xiangyu Zhang:
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning. - Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant:
UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining. - Xiaoqi Wang, Han-Wei Shen:
GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks. - Kei Sen Fong, Shelvia Wongso, Mehul Motani:
Rethinking Symbolic Regression: Morphology and Adaptability in the Context of Evolutionary Algorithms. - Danqing Wang, Fei Ye, Hao Zhou:
On Pre-training Language Model for Antibody. - Shanka Subhra Mondal, Taylor Whittington Webb, Jonathan Cohen:
Learning to reason over visual objects. - Junsu Kim, Younggyo Seo, Sungsoo Ahn, Kyunghwan Son, Jinwoo Shin:
Imitating Graph-Based Planning with Goal-Conditioned Policies. - Jeff Z. HaoChen, Tengyu Ma:
A theoretical study of inductive biases in contrastive learning. - Nuoya Xiong, Wei Chen:
Combinatorial Pure Exploration of Causal Bandits. - Andy Liu, Hao Zhu, Emmy Liu, Yonatan Bisk, Graham Neubig:
Computational Language Acquisition with Theory of Mind. - Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, Kaili Ma, Han Yang, Peilin Zhao, Bo Han, James Cheng:
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. - Yuhong Li, Tianle Cai, Yi Zhang, Deming Chen, Debadeepta Dey:
What Makes Convolutional Models Great on Long Sequence Modeling? - Gabriel Ilharco, Marco Túlio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi:
Editing models with task arithmetic. - Gautam Singh, Yeongbin Kim, Sungjin Ahn:
Neural Systematic Binder. - Weixi Feng, Xuehai He, Tsu-Jui Fu, Varun Jampani, Arjun R. Akula, Pradyumna Narayana, Sugato Basu, Xin Eric Wang, William Yang Wang:
Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis. - Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh:
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories. - Zehao Dong, Weidong Cao, Muhan Zhang, Dacheng Tao, Yixin Chen, Xuan Zhang:
CktGNN: Circuit Graph Neural Network for Electronic Design Automation. - Deyu Bo, Chuan Shi, Lele Wang, Renjie Liao:
Specformer: Spectral Graph Neural Networks Meet Transformers. - Abulhair Saparov, He He:
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought. - Amine Mohamed Aboussalah, Min-Jae Kwon, Raj G. Patel, Cheng Chi, Chi-Guhn Lee:
Recursive Time Series Data Augmentation. - Aaron Palmer, Zhiyi Chi, Derek Aguiar, Jinbo Bi:
Auto-Encoding Goodness of Fit. - Asher Trockman, Devin Willmott, J. Zico Kolter:
Understanding the Covariance Structure of Convolutional Filters. - Tao Huang, Yuan Zhang, Shan You, Fei Wang, Chen Qian, Jian Cao, Chang Xu:
Masked Distillation with Receptive Tokens. - Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan:
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. - Lingfeng Shen, Ze Zhang, Haiyun Jiang, Ying Chen:
TextShield: Beyond Successfully Detecting Adversarial Sentences in text classification. - Qiyang Li, Aviral Kumar, Ilya Kostrikov, Sergey Levine:
Efficient Deep Reinforcement Learning Requires Regulating Overfitting. - Ming Yin, Mengdi Wang, Yu-Xiang Wang:
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. - Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare:
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. - Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Fred Zhang, Qiuyi Zhang, Samson Zhou:
Robust Algorithms on Adaptive Inputs from Bounded Adversaries. - Chunhui Zhang, Yijun Tian, Mingxuan Ju, Zheyuan Liu, Yanfang Ye, Nitesh V. Chawla, Chuxu Zhang:
Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization. - Ziang Chen, Jialin Liu, Xinshang Wang, Wotao Yin:
On Representing Mixed-Integer Linear Programs by Graph Neural Networks. - Hong-You Chen, Cheng-Hao Tu, Ziwei Li, Han-Wei Shen, Wei-Lun Chao:
On the Importance and Applicability of Pre-Training for Federated Learning. - Filipe de Avila Belbute-Peres, J. Zico Kolter:
Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth. - Huancheng Chen, Chianing Wang, Haris Vikalo:
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation. - Zixuan Liu, Ziqiao Wang, Hongyu Guo, Yongyi Mao:
Over-Training with Mixup May Hurt Generalization. - Shijie Geng, Jianbo Yuan, Yu Tian, Yuxiao Chen, Yongfeng Zhang:
HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention. - Jake Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel:
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. - Griffin Floto, Stefan Kremer, Mihai Nica:
The Tilted Variational Autoencoder: Improving Out-of-Distribution Detection. - Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Curtis Mozer, Nicolas Heess, Yoshua Bengio:
Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. - Zihan Zhou, Animesh Garg:
Learning Achievement Structure for Structured Exploration in Domains with Sparse Reward. - Peifeng Wang, Aaron Chan, Filip Ilievski, Muhao Chen, Xiang Ren:
PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales. - Shunta Akiyama, Taiji Suzuki:
Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods. - Jack Merullo, Louis Castricato, Carsten Eickhoff, Ellie Pavlick:
Linearly Mapping from Image to Text Space. - Shikhar Murty, Pratyusha Sharma, Jacob Andreas, Christopher D. Manning:
Characterizing intrinsic compositionality in transformers with Tree Projections. - Lu Han, Han-Jia Ye, De-Chuan Zhan:
Augmentation Component Analysis: Modeling Similarity via the Augmentation Overlaps. - Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas:
Replicable Bandits. - Fred Lu, Edward Raff, Francis Ferraro:
Neural Bregman Divergences for Distance Learning. - Hongyan Chang, Reza Shokri:
Bias Propagation in Federated Learning. - Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson, Anca D. Dragan, Daniel S. Brown:
Causal Confusion and Reward Misidentification in Preference-Based Reward Learning. - Jinhao Jiang, Kun Zhou, Xin Zhao, Ji-Rong Wen:
UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph. - Shicong Cen, Yuejie Chi, Simon Shaolei Du, Lin Xiao:
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games. - Erdem Koyuncu:
Memorization Capacity of Neural Networks with Conditional Computation. - Ruixuan Yan, Yunshi Wen, Debarun Bhattacharjya, Ronny Luss, Tengfei Ma, Achille Fokoue, Anak Agung Julius:
Weighted Clock Logic Point Process. - Pu Hua, Yubei Chen, Huazhe Xu:
Simple Emergent Action Representations from Multi-Task Policy Training. - Akihiro Nakano, Masahiro Suzuki, Yutaka Matsuo:
Interaction-Based Disentanglement of Entities for Object-Centric World Models. - Youngjoong Kwon, Dahun Kim, Duygu Ceylan, Henry Fuchs:
Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling. - Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet:
Federated Neural Bandits. - Nan Shao, Zefan Cai, Hanwei Xu, Chonghua Liao, Yanan Zheng, Zhilin Yang:
Compositional Task Representations for Large Language Models. - Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, Behnam Neyshabur:
REPAIR: REnormalizing Permuted Activations for Interpolation Repair. - Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, Mingyuan Zhou:
Diffusion-GAN: Training GANs with Diffusion. - Bilal Alsallakh, David Yan, Narine Kokhlikyan, Vivek Miglani, Orion Reblitz-Richardson, Pamela Bhattacharya:
Mind the Pool: Convolutional Neural Networks Can Overfit Input Size. - Alexander Detkov, Mohammad Salameh, Muhammad Fetrat Qharabagh, Jialin Zhang, Robin Luwei, Shangling Jui, Di Niu:
Reparameterization through Spatial Gradient Scaling. - Haoyu Peter Wang, Pan Li:
Unsupervised Learning for Combinatorial Optimization Needs Meta Learning. - Liam H. Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein:
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. - Etai Littwin, Greg Yang:
Adaptive Optimization in the ∞-Width Limit. - Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger:
Broken Neural Scaling Laws. - Sheng Liu, Xu Zhang, Nitesh Sekhar, Yue Wu, Prateek Singhal, Carlos Fernandez-Granda:
Avoiding spurious correlations via logit correction. - Ruiquan Huang, Jing Yang, Yingbin Liang:
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-Free RL. - Gihyun Kwon, Jong Chul Ye:
Diffusion-based Image Translation using disentangled style and content representation. - Jiangyuan Li, Thanh Van Nguyen, Chinmay Hegde, Raymond K. W. Wong:
Implicit Regularization for Group Sparsity. - Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, Jimmy Ba:
Large Language Models are Human-Level Prompt Engineers. - Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh:
Pruning Deep Neural Networks from a Sparsity Perspective. - Runsheng Yu, Weiyu Chen, Xinrun Wang, James T. Kwok:
Enhancing Meta Learning via Multi-Objective Soft Improvement Functions. - José Lezama, Tim Salimans, Lu Jiang, Huiwen Chang, Jonathan Ho, Irfan Essa:
Discrete Predictor-Corrector Diffusion Models for Image Synthesis. - Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh:
OPTQ: Accurate Quantization for Generative Pre-trained Transformers. - Sungyoon Lee, Cheongjae Jang:
A new characterization of the edge of stability based on a sharpness measure aware of batch gradient distribution. - Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban, Kostas Daniilidis:
SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space. - Zixuan Ke, Yijia Shao, Haowei Lin, Tatsuya Konishi, Gyuhak Kim, Bing Liu:
Continual Pre-training of Language Models. - Alex Gu, Songtao Lu, Parikshit Ram, Tsui-Wei Weng:
Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning. - Zeyuan Allen-Zhu, Yuanzhi Li:
Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions. - Gang Li, Yang Li:
Spotlight: Mobile UI Understanding using Vision-Language Models with a Focus. - Stephen Tian, Chelsea Finn, Jiajun Wu:
A Control-Centric Benchmark for Video Prediction. - Marc Anton Finzi, Andres Potapczynski, Matthew Choptuik, Andrew Gordon Wilson:
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks. - Frederik Kunstner, Jacques Chen, Jonathan Wilder Lavington, Mark Schmidt:
Noise Is Not the Main Factor Behind the Gap Between Sgd and Adam on Transformers, But Sign Descent Might Be. - Michael S. Albergo, Eric Vanden-Eijnden:
Building Normalizing Flows with Stochastic Interpolants. - James Beetham, Navid Kardan, Ajmal Saeed Mian, Mubarak Shah:
Dual Student Networks for Data-Free Model Stealing. - Mingu Lee, Saurabh Pitre, Tianyu Jiang, Pierre-David Letourneau, Matthew J. Morse, Kanghwan Jang, Joseph Soriaga, Parham Noorzad, Hsin-Pai Cheng, Christopher Lott:
Composite Slice Transformer: An Efficient Transformer with Composition of Multi-Scale Multi-Range Attentions. - Ozgur Guldogan, Yuchen Zeng, Jy-yong Sohn, Ramtin Pedarsani, Kangwook Lee:
Equal Improvability: A New Fairness Notion Considering the Long-term Impact. - Qi Zeng, Yash Kothari, Spencer H. Bryngelson, Florian Schäfer:
Competitive Physics Informed Networks. - Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, Ashish Sabharwal:
Decomposed Prompting: A Modular Approach for Solving Complex Tasks. - Sizhe Chen, Geng Yuan, Xinwen Cheng, Yifan Gong, Minghai Qin, Yanzhi Wang, Xiaolin Huang:
Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors. - Michael Zhang, Khaled Kamal Saab, Michael Poli, Tri Dao, Karan Goel, Christopher Ré:
Effectively Modeling Time Series with Simple Discrete State Spaces. - Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam:
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. - Yihao Feng, Shentao Yang, Shujian Zhang, Jianguo Zhang, Caiming Xiong, Mingyuan Zhou, Huan Wang:
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems. - Hrayr Harutyunyan, Ankit Singh Rawat, Aditya Krishna Menon, Seungyeon Kim, Sanjiv Kumar:
Supervision Complexity and its Role in Knowledge Distillation. - Jie Ren, Han Xu, Yuxuan Wan, Xingjun Ma, Lichao Sun, Jiliang Tang:
Transferable Unlearnable Examples. - Tao Yu, Christopher De Sa:
Random Laplacian Features for Learning with Hyperbolic Space. - Hongming Zhang, Chenjun Xiao, Han Wang, Jun Jin, Bo Xu, Martin Müller:
Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experience Replay. - Kevin Muyuan Xia, Yushu Pan, Elias Bareinboim:
Neural Causal Models for Counterfactual Identification and Estimation. - Lingkai Kong, Yuqing Wang, Molei Tao:
Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport. - Xianghao Kong, Rob Brekelmans, Greg Ver Steeg:
Information-Theoretic Diffusion. - Kareem Ahmed, Zhe Zeng, Mathias Niepert, Guy Van den Broeck:
SIMPLE: A Gradient Estimator for k-Subset Sampling. - Xiangyu Chen, Varsha Kishore, Kilian Q. Weinberger:
Learning Iterative Neural Optimizers for Image Steganography. - Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson:
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. - Weijie Liu, Jiahao Xie, Chao Zhang, Makoto Yamada, Nenggan Zheng, Hui Qian:
Robust Graph Dictionary Learning. - Zheng Dai, David Gifford:
Fundamental limits on the robustness of image classifiers. - Nikunj Saunshi, Arushi Gupta, Mark Braverman, Sanjeev Arora:
Understanding Influence Functions and Datamodels via Harmonic Analysis. - Bairu Hou, Jinghan Jia, Yihua Zhang, Guanhua Zhang, Yang Zhang, Sijia Liu, Shiyu Chang:
TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization. - Linara Adilova, Bernhard C. Geiger, Asja Fischer:
Information Plane Analysis for Dropout Neural Networks. - Yiqun Wang, Yuning Shen, Shi Chen, Lihao Wang, Fei Ye, Hao Zhou:
Learning Harmonic Molecular Representations on Riemannian Manifold. - Samuel Neumann, Sungsu Lim, Ajin George Joseph, Yangchen Pan, Adam White, Martha White:
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement. - Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi S. Jaakkola:
Efficiently Controlling Multiple Risks with Pareto Testing. - Xingzi Xu, Ali Hasan, Khalil Elkhalil, Jie Ding, Vahid Tarokh:
Characteristic Neural Ordinary Differential Equation. - Qinsheng Zhang, Yongxin Chen:
Fast Sampling of Diffusion Models with Exponential Integrator. - Hong-Min Chu, Jonas Geiping, Liam H. Fowl, Micah Goldblum, Tom Goldstein:
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. - Matthew J. Tilley, Michelle Miller, David Freedman:
Artificial Neuronal Ensembles with Learned Context Dependent Gating. - Jianshu Chen:
Learning Language Representations with Logical Inductive Bias. - Yiwen Kou, Zixiang Chen, Yuan Cao, Quanquan Gu:
How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study. - Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, Neil Shah:
Empowering Graph Representation Learning with Test-Time Graph Transformation. - Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Provable Robustness against Wasserstein Distribution Shifts via Input Randomization. - Huan-Hsin Tseng, Hsin-Yi Lin, Kuo-Hsuan Hung, Yu Tsao:
Interpretations of Domain Adaptations via Layer Variational Analysis. - Francisco Vargas, Will Sussman Grathwohl, Arnaud Doucet:
Denoising Diffusion Samplers. - Max Zimmer, Christoph Spiegel, Sebastian Pokutta:
How I Learned to Stop Worrying and Love Retraining. - Siqi Miao, Yunan Luo, Mia Liu, Pan Li:
Interpretable Geometric Deep Learning via Learnable Randomness Injection. - Tennison Liu, Zhaozhi Qian, Jeroen Berrevoets, Mihaela van der Schaar:
GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure. - Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa, Mike Lewis, Amjad Almahairi:
Progressive Prompts: Continual Learning for Language Models. - Shahana Ibrahim, Tri Nguyen, Xiao Fu:
Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization. - Yingzhen Yang, Ping Li:
Projective Proximal Gradient Descent for Nonconvex Nonsmooth Optimization: Fast Convergence Without Kurdyka-Lojasiewicz (KL) Property. - Kefan Dong, Tengyu Ma:
First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains. - Henry Conklin, Kenny Smith:
Compositionality with Variation Reliably Emerges in Neural Networks. - Meng Cao, Mehdi Fatemi, Jackie C. K. Cheung, Samira Shabanian:
Systematic Rectification of Language Models via Dead-end Analysis. - Edo Dotan, Yonatan Belinkov, Oren Avram, Elya Wygoda, Noa Ecker, Michael Alburquerque, Omri Keren, Gil Loewenthal, Tal Pupko:
Multiple sequence alignment as a sequence-to-sequence learning problem. - Yinlam Chow, Aza Tulepbergenov, Ofir Nachum, Dhawal Gupta, Moonkyung Ryu, Mohammad Ghavamzadeh, Craig Boutilier:
A Mixture-of-Expert Approach to RL-based Dialogue Management. - Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Miguel Ángel Bautista, Joshua M. Susskind:
f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation. - Beren Millidge, Yuhang Song, Tommaso Salvatori, Thomas Lukasiewicz, Rafal Bogacz:
Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning. - Beren Millidge, Yuhang Song, Tommaso Salvatori, Thomas Lukasiewicz, Rafal Bogacz:
A Theoretical Framework for Inference and Learning in Predictive Coding Networks. - Alexander B. Atanasov, Blake Bordelon, Sabarish Sainathan, Cengiz Pehlevan:
The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes. - Brandon Trabucco, Gunnar A. Sigurdsson, Robinson Piramuthu, Gaurav S. Sukhatme, Ruslan Salakhutdinov:
A Simple Approach for Visual Room Rearrangement: 3D Mapping and Semantic Search. - Ronghang Zhu, Xiang Yu, Sheng Li:
Progressive Mix-Up for Few-Shot Supervised Multi-Source Domain Transfer. - Kewei Cheng, Nesreen K. Ahmed, Yizhou Sun:
Neural Compositional Rule Learning for Knowledge Graph Reasoning. - Koosha Khalvati, Samantha Johnson, Stefan Mihalas, Michael A. Buice:
Efficient approximation of neural population structure and correlations with probabilistic circuits. - Anne Harrington, Vasha DuTell, Ayush Tewari, Mark Hamilton, Simon Stent, Ruth Rosenholtz, William T. Freeman:
Exploring perceptual straightness in learned visual representations. - Jiefeng Chen, Timothy Nguyen, Dilan Görür, Arslan Chaudhry:
Is Forgetting Less a Good Inductive Bias for Forward Transfer? - Siqi Zeng, Remi Tachet des Combes, Han Zhao:
Learning Structured Representations by Embedding Class Hierarchy. - Zhuyun Dai, Vincent Y. Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B. Hall, Ming-Wei Chang:
Promptagator: Few-shot Dense Retrieval From 8 Examples. - Tahereh Toosi, Elias B. Issa:
Brain-like representational straightening of natural movies in robust feedforward neural networks. - AmirEhsan Khorashadizadeh, Anadi Chaman, Valentin Debarnot, Ivan Dokmanic:
FunkNN: Neural Interpolation for Functional Generation. - Rattana Pukdee, Dylan Sam, Pradeep Kumar Ravikumar, Nina Balcan:
Label Propagation with Weak Supervision. - Jiayi Wei, Greg Durrett, Isil Dillig:
TypeT5: Seq2seq Type Inference using Static Analysis. - Bhargavi Paranjape, Pradeep Dasigi, Vivek Srikumar, Luke Zettlemoyer, Hannaneh Hajishirzi:
AGRO: Adversarial discovery of error-prone Groups for Robust Optimization. - Yuan Yang, Faramarz Fekri, James Clayton Kerce, Ali Payani:
LogicDP: Creating Labels for Graph Data via Inductive Logic Programming. - Kaixin Wang, Kuangqi Zhou, Bingyi Kang, Jiashi Feng, Shuicheng Yan:
Revisiting Intrinsic Reward for Exploration in Procedurally Generated Environments. - Jan Robine, Marc Höftmann, Tobias Uelwer, Stefan Harmeling:
Transformer-based World Models Are Happy With 100k Interactions. - Aaron Traylor, Roman Feiman, Ellie Pavlick:
Can Neural Networks Learn Implicit Logic from Physical Reasoning? - Stephen Marcus McAleer, Gabriele Farina, Marc Lanctot, Tuomas Sandholm:
ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret. - Justin D. Li, Matus Telgarsky:
On Achieving Optimal Adversarial Test Error. - Jun-Kun Wang, Andre Wibisono:
Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation. - James Henderson, Fabio Fehr:
A VAE for Transformers with Nonparametric Variational Information Bottleneck. - Devon Jarvis, Richard Klein, Benjamin Rosman, Andrew M. Saxe:
On The Specialization of Neural Modules. - Chen Liang, Haoming Jiang, Zheng Li, Xianfeng Tang, Bing Yin, Tuo Zhao:
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers. - Albert Yu, Raymond J. Mooney:
Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks. - Dimitri von Rütte, Luca Biggio, Yannic Kilcher, Thomas Hofmann:
FIGARO: Controllable Music Generation using Learned and Expert Features. - Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, Jason Wei:
Language models are multilingual chain-of-thought reasoners. - Zhiqing Sun, Xuezhi Wang, Yi Tay, Yiming Yang, Denny Zhou:
Recitation-Augmented Language Models. - Sandeep Silwal, Sara Ahmadian, Andrew Nystrom, Andrew McCallum, Deepak Ramachandran, Seyed Mehran Kazemi:
KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals. - Minae Kwon, Sang Michael Xie, Kalesha Bullard, Dorsa Sadigh:
Reward Design with Language Models. - Bowen Lei, Ruqi Zhang, Dongkuan Xu, Bani K. Mallick:
Calibrating the Rigged Lottery: Making All Tickets Reliable. - Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi:
A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. - Shushan Wu, Huimin Cheng, Jiazhang Cai, Ping Ma, Wenxuan Zhong:
Subsampling in Large Graphs Using Ricci Curvature. - Jihwan Jeong, Xiaoyu Wang, Michael Gimelfarb, Hyunwoo Kim, Baher Abdulhai, Scott Sanner:
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization. - Linfeng Zhao, Huazhe Xu, Lawson L. S. Wong:
Scaling up and Stabilizing Differentiable Planning with Implicit Differentiation. - Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, Hanjun Dai:
Score-based Continuous-time Discrete Diffusion Models. - Kaizhe Hu, Ray Chen Zheng, Yang Gao, Huazhe Xu:
Decision Transformer under Random Frame Dropping. - Aleksandar Taranovic, Andras Gabor Kupcsik, Niklas Freymuth, Gerhard Neumann:
Adversarial Imitation Learning with Preferences. - Ruijie Zheng, Xiyao Wang, Huazhe Xu, Furong Huang:
Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function. - Kai Xu, Georgi Ganev, Emile Joubert, Rees Davison, Olivier Van Acker, Luke Robinson:
Synthetic Data Generation of Many-to-Many Datasets via Random Graph Generation. - Edo Cohen-Karlik, Itamar Menuhin-Gruman, Raja Giryes, Nadav Cohen, Amir Globerson:
Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets. - Kazuki Irie, Jürgen Schmidhuber:
Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules. - Xinran Gu, Kaifeng Lyu, Longbo Huang, Sanjeev Arora:
Why (and When) does Local SGD Generalize Better than SGD? - Jeremiah Birrell, Yannis Pantazis, Paul Dupuis, Luc Rey-Bellet, Markos A. Katsoulakis:
Function-space regularized Rényi divergences. - Nikolaos Gkanatsios, Mayank Singh, Zhaoyuan Fang, Shubham Tulsiani, Katerina Fragkiadaki:
Analogy-Forming Transformers for Few-Shot 3D Parsing. - Hossein Mirzaei, Mohammadreza Salehi, Sajjad Shahabi, Efstratios Gavves, Cees G. M. Snoek, Mohammad Sabokrou, Mohammad Hossein Rohban:
Fake It Until You Make It : Towards Accurate Near-Distribution Novelty Detection. - Syed Zawad, Cheng Li, Zhewei Yao, Elton Zheng, Yuxiong He, Feng Yan:
DySR: Adaptive Super-Resolution via Algorithm and System Co-design. - Linfeng Zhao, Xupeng Zhu, Lingzhi Kong, Robin Walters, Lawson L. S. Wong:
Integrating Symmetry into Differentiable Planning with Steerable Convolutions. - Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang:
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. - Yuepeng Yang, Cong Ma:
O(T-1 Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games. - Sophia Sanborn, Christian Shewmake, Bruno A. Olshausen, Christopher J. Hillar:
Bispectral Neural Networks. - Konstantinos E. Nikolakakis, Farzin Haddadpour, Amin Karbasi, Dionysios S. Kalogerias:
Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD. - Mengdi Xu, Yuchen Lu, Yikang Shen, Shun Zhang, Ding Zhao, Chuang Gan:
Hyper-Decision Transformer for Efficient Online Policy Adaptation. - Tim Seyde, Peter Werner, Wilko Schwarting, Igor Gilitschenski, Martin A. Riedmiller, Daniela Rus, Markus Wulfmeier:
Solving Continuous Control via Q-learning. - Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, Yaniv Taigman:
Make-A-Video: Text-to-Video Generation without Text-Video Data. - Jessica Maghakian, Paul Mineiro, Kishan Panaganti, Mark Rucker, Akanksha Saran, Cheng Tan:
Personalized Reward Learning with Interaction-Grounded Learning (IGL). - Fivos Kalogiannis, Ioannis Panageas, Emmanouil V. Vlatakis-Gkaragkounis:
Towards convergence to Nash equilibria in two-team zero-sum games. - Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dalibard, Chris Lu, Satinder Singh, Sebastian Flennerhag:
Discovering Evolution Strategies via Meta-Black-Box Optimization. - Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, Dawn Song:
DensePure: Understanding Diffusion Models for Adversarial Robustness. - Jonas Linkerhägner, Niklas Freymuth, Paul Maria Scheikl, Franziska Mathis-Ullrich, Gerhard Neumann:
Grounding Graph Network Simulators using Physical Sensor Observations. - Raghav Singhal, Mark Goldstein, Rajesh Ranganath:
Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions. - Chris Lin, Hugh Chen, Chanwoo Kim, Su-In Lee:
Contrastive Corpus Attribution for Explaining Representations. - Zheng Dong, Xiuyuan Cheng, Yao Xie:
Spatio-temporal point processes with deep non-stationary kernels. - Michael Kamp, Jonas Fischer, Jilles Vreeken:
Federated Learning from Small Datasets. - Lin Guan, Karthik Valmeekam, Subbarao Kambhampati:
Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferences. - Renyu Zhang, Aly A. Khan, Robert L. Grossman, Yuxin Chen:
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing. - Richa Rastogi, Yair Schiff, Alon Hacohen, Zhaozhi Li, Ian Lee, Yuntian Deng, Mert R. Sabuncu, Volodymyr Kuleshov:
Semi-Parametric Inducing Point Networks and Neural Processes. - Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae:
DAG Learning on the Permutahedron. - Gustav Bredell, Kyriakos Flouris, Krishna Chaitanya, Ertunc Erdil, Ender Konukoglu:
Explicitly Minimizing the Blur Error of Variational Autoencoders. - Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, Jianzhu Ma:
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction. - Arna Ghosh, Yuhan Helena Liu, Guillaume Lajoie, Konrad P. Körding, Blake Aaron Richards:
How gradient estimator variance and bias impact learning in neural networks. - Yazhe Li, Jörg Bornschein, Marcus Hutter:
Evaluating Representations with Readout Model Switching. - Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji:
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. - Jiaming Song, Arash Vahdat, Morteza Mardani, Jan Kautz:
Pseudoinverse-Guided Diffusion Models for Inverse Problems. - Hongyi Chen, Yilun Du, Yiye Chen, Joshua B. Tenenbaum, Patricio A. Vela:
Planning with Sequence Models through Iterative Energy Minimization. - Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem:
Verifying the Union of Manifolds Hypothesis for Image Data. - Fahad Sarfraz, Elahe Arani, Bahram Zonooz:
Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning. - Daksh Idnani, Vivek Madan, Naman Goyal, David J. Schwab, Ramakrishna Vedantam:
Don't forget the nullspace! Nullspace occupancy as a mechanism for out of distribution failure. - Xiaojun Guo, Yifei Wang, Tianqi Du, Yisen Wang:
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond. - Yang Cai, Weiqiang Zheng:
Accelerated Single-Call Methods for Constrained Min-Max Optimization. - Ali Ramezani-Kebrya, Kimon Antonakopoulos, Igor Krawczuk, Justin Deschenaux, Volkan Cevher:
Distributed Extra-gradient with Optimal Complexity and Communication Guarantees. - Haotian Fu, Jiayu Yao, Omer Gottesman, Finale Doshi-Velez, George Konidaris:
Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs. - Wilka Carvalho, Angelos Filos, Richard L. Lewis, Honglak Lee, Satinder Singh:
Composing Task Knowledge With Modular Successor Feature Approximators. - Sizhe Li, Zhiao Huang, Tao Chen, Tao Du, Hao Su, Joshua B. Tenenbaum, Chuang Gan:
DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics. - Jiacheng Li, Ninghui Li, Bruno Ribeiro:
Effective passive membership inference attacks in federated learning against overparameterized models. - Sheng Zhang, Hao Cheng, Jianfeng Gao, Hoifung Poon:
Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning. - Zhen Lin, Shubhendu Trivedi, Jimeng Sun:
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks. - Matko Bosnjak, Pierre Harvey Richemond, Nenad Tomasev, Florian Strub, Jacob C. Walker, Felix Hill, Lars Holger Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic:
SemPPL: Predicting Pseudo-Labels for Better Contrastive Representations. - Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith:
Differentially Private Adaptive Optimization with Delayed Preconditioners. - Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar, Santiago Castro, Julius Kunze, Dumitru Erhan:
Phenaki: Variable Length Video Generation from Open Domain Textual Descriptions. - Harsh Mehta, Ankit Gupta, Ashok Cutkosky, Behnam Neyshabur:
Long Range Language Modeling via Gated State Spaces. - Yufei Cui, Ziquan Liu, Xiangyu Liu, Xue Liu, Cong Wang, Tei-Wei Kuo, Chun Jason Xue, Antoni B. Chan:
Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images. - Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron C. Courville, Marc G. Bellemare:
Investigating Multi-task Pretraining and Generalization in Reinforcement Learning. - Ben Zandonati, Adrian Alan Pol, Maurizio Pierini, Olya Sirkin, Tal Kopetz:
FIT: A Metric for Model Sensitivity. - Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum:
Transfer Learning with Deep Tabular Models. - Alexandra Peste, Adrian Vladu, Eldar Kurtic, Christoph H. Lampert, Dan Alistarh:
CrAM: A Compression-Aware Minimizer. - Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu:
Understanding Train-Validation Split in Meta-Learning with Neural Networks. - Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann:
Revisiting Robustness in Graph Machine Learning. - Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin David Haeffele, Donald Geman, René Vidal:
Variational Information Pursuit for Interpretable Predictions. - Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme Ruiz, Basil Mustafa, Joshua Ainslie, Yi Tay, Mostafa Dehghani, Neil Houlsby:
Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints. - Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess, Jon Scholz, Yusuf Aytar:
Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation. - Zihao Wang, Yangqiu Song, Ginny Y. Wong, Simon See:
Logical Message Passing Networks with One-hop Inference on Atomic Formulas. - Emily Silcock, Luca D'Amico-Wong, Jinglin Yang, Melissa Dell:
Noise-Robust De-Duplication at Scale. - Jonathan Hayase, Sewoong Oh:
Few-shot Backdoor Attacks via Neural Tangent Kernels. - Bruno Mlodozeniec, Matthias Reisser, Christos Louizos:
Hyperparameter Optimization through Neural Network Partitioning. - Bo Zhao, Iordan Ganev, Robin Walters, Rose Yu, Nima Dehmamy:
Symmetries, Flat Minima, and the Conserved Quantities of Gradient Flow. - Swarnadeep Saha, Shiyue Zhang, Peter Hase, Mohit Bansal:
Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees. - Shun Zhang, Zhenfang Chen, Yikang Shen, Mingyu Ding, Joshua B. Tenenbaum, Chuang Gan:
Planning with Large Language Models for Code Generation. - Kaitlin Maile, Dennis George Wilson, Patrick Forré:
Equivariance-aware Architectural Optimization of Neural Networks. - Jun-Kun Wang, Andre Wibisono:
Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time. - Xihuai Wang, Zheng Tian, Ziyu Wan, Ying Wen, Jun Wang, Weinan Zhang:
Order Matters: Agent-by-agent Policy Optimization. - Zijian Liu, Ta Duy Nguyen, Alina Ene, Huy L. Nguyen:
On the Convergence of AdaGrad(Norm) on ℝd: Beyond Convexity, Non-Asymptotic Rate and Acceleration. - Shuang Li, William J. Swartworth, Martin Takác, Deanna Needell, Robert M. Gower:
SP2 : A Second Order Stochastic Polyak Method. - Jinhua Zhu, Yue Wang, Lijun Wu, Tao Qin, Wengang Zhou, Tie-Yan Liu, Houqiang Li:
Making Better Decision by Directly Planning in Continuous Control. - Chang Li, Dongjin Song, Dacheng Tao:
HiT-MDP: Learning the SMDP option framework on MDPs with Hidden Temporal Embeddings. - Nicholas Carlini, Florian Tramèr, Krishnamurthy (Dj) Dvijotham, Leslie Rice, Mingjie Sun, J. Zico Kolter:
(Certified!!) Adversarial Robustness for Free! - Biswadeep Chakraborty, Saibal Mukhopadhyay:
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles. - Emanuele Palumbo, Imant Daunhawer, Julia E. Vogt:
MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises. - Ashish R. Mittal, Sunita Sarawagi, Preethi Jyothi:
In-Situ Text-Only Adaptation of Speech Models with Low-Overhead Speech Imputations. - Tobit Klug, Reinhard Heckel:
Scaling Laws For Deep Learning Based Image Reconstruction. - Ivona Najdenkoska, Xiantong Zhen, Marcel Worring:
Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning. - Tsun-Hsuan Wang, Pingchuan Ma, Andrew Everett Spielberg, Zhou Xian, Hao Zhang, Joshua B. Tenenbaum, Daniela Rus, Chuang Gan:
SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments. - Thy Dinh Nguyen, Anamay Chaturvedi, Huy L. Nguyen:
Improved Learning-augmented Algorithms for k-means and k-medians Clustering.