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Spotlight
Tue 5:25 Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
Seungyul Han, Youngchul Sung
Spotlight
Tue 5:35 Low-Rank Sinkhorn Factorization
Meyer Scetbon, Marco Cuturi, Gabriel Peyré
Spotlight
Tue 5:40 Newton Method over Networks is Fast up to the Statistical Precision
Amir Daneshmand, Gesualdo Scutari, Pavel Dvurechenskii, Alexander Gasnikov
Oral
Tue 6:00 Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach
Tom Fei, Zhuoran Yang, Zhaoran Wang
Spotlight
Tue 6:25 Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs
Tolga Ergen, Mert Pilanci
Spotlight
Tue 6:25 Self-Tuning for Data-Efficient Deep Learning
Ximei Wang, Jinghan Gao, Mingsheng Long, Jianmin Wang
Spotlight
Tue 6:30 Connecting Sphere Manifolds Hierarchically for Regularization
Damien Scieur, Youngsung Kim
Spotlight
Tue 6:35 A Functional Perspective on Learning Symmetric Functions with Neural Networks
Aaron Zweig, Joan Bruna
Spotlight
Tue 6:45 Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More
Johannes Klicpera, Marten Lienen, Stephan Günnemann
Spotlight
Tue 7:20 Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization
Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama
Spotlight
Tue 7:40 Autoencoder Image Interpolation by Shaping the Latent Space
Alon Oring, Zohar Yakhini, Yacov Hel-Or
Spotlight
Tue 7:45 Distributed Second Order Methods with Fast Rates and Compressed Communication
Rustem Islamov, Xun Qian, Peter Richtarik
Spotlight
Tue 7:45 DeepWalking Backwards: From Embeddings Back to Graphs
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Babis Tsourakakis
Spotlight
Tue 7:45 Better Training using Weight-Constrained Stochastic Dynamics
Benedict Leimkuhler, Tiffany Vlaar, Timothée Pouchon, Amos Storkey
Poster
Tue 9:00 Better Training using Weight-Constrained Stochastic Dynamics
Benedict Leimkuhler, Tiffany Vlaar, Timothée Pouchon, Amos Storkey
Poster
Tue 9:00 Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
Seungyul Han, Youngchul Sung
Poster
Tue 9:00 Distributed Second Order Methods with Fast Rates and Compressed Communication
Rustem Islamov, Xun Qian, Peter Richtarik
Poster
Tue 9:00 Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs
Tolga Ergen, Mert Pilanci
Poster
Tue 9:00 Low-Rank Sinkhorn Factorization
Meyer Scetbon, Marco Cuturi, Gabriel Peyré
Poster
Tue 9:00 Connecting Sphere Manifolds Hierarchically for Regularization
Damien Scieur, Youngsung Kim
Poster
Tue 9:00 Self-Tuning for Data-Efficient Deep Learning
Ximei Wang, Jinghan Gao, Mingsheng Long, Jianmin Wang
Poster
Tue 9:00 DeepWalking Backwards: From Embeddings Back to Graphs
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Babis Tsourakakis
Poster
Tue 9:00 Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization
Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama
Poster
Tue 9:00 Newton Method over Networks is Fast up to the Statistical Precision
Amir Daneshmand, Gesualdo Scutari, Pavel Dvurechenskii, Alexander Gasnikov
Poster
Tue 9:00 Autoencoder Image Interpolation by Shaping the Latent Space
Alon Oring, Zohar Yakhini, Yacov Hel-Or
Poster
Tue 9:00 Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More
Johannes Klicpera, Marten Lienen, Stephan Günnemann
Poster
Tue 9:00 A Functional Perspective on Learning Symmetric Functions with Neural Networks
Aaron Zweig, Joan Bruna
Poster
Tue 9:00 Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach
Tom Fei, Zhuoran Yang, Zhaoran Wang
Spotlight
Tue 17:20 Stabilizing Equilibrium Models by Jacobian Regularization
Shaojie Bai, Vladlen Koltun, Zico Kolter
Spotlight
Tue 17:25 Convex Regularization in Monte-Carlo Tree Search
Tuan Q Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen
Spotlight
Tue 17:30 From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
Julien Perolat, Remi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro Ortega, Neil Burch, Thomas Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls
Spotlight
Tue 17:30 Offline Reinforcement Learning with Fisher Divergence Critic Regularization
Ilya Kostrikov, Rob Fergus, Jonathan Tompson, Ofir Nachum
Spotlight
Tue 17:45 Discovering symbolic policies with deep reinforcement learning
Mikel Landajuela Larma, Brenden Petersen, Sookyung Kim, Claudio Santiago, Ruben Glatt, Nathan Mundhenk, Jacob Pettit, Daniel Faissol
Spotlight
Tue 18:20 An Identifiable Double VAE For Disentangled Representations
Graziano Mita, Maurizio Filippone, Pietro Michiardi
Spotlight
Tue 18:20 EfficientNetV2: Smaller Models and Faster Training
Mingxing Tan, Quoc Le
Oral
Tue 19:00 Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Bahar Taskesen, Man Chung Yue, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen
Spotlight
Tue 19:30 Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
Jongwook Choi, Archit Sharma, Honglak Lee, Sergey Levine, Shixiang Gu
Poster
Tue 21:00 EfficientNetV2: Smaller Models and Faster Training
Mingxing Tan, Quoc Le
Poster
Tue 21:00 Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
Jongwook Choi, Archit Sharma, Honglak Lee, Sergey Levine, Shixiang Gu
Poster
Tue 21:00 Offline Reinforcement Learning with Fisher Divergence Critic Regularization
Ilya Kostrikov, Rob Fergus, Jonathan Tompson, Ofir Nachum
Poster
Tue 21:00 Stabilizing Equilibrium Models by Jacobian Regularization
Shaojie Bai, Vladlen Koltun, Zico Kolter
Poster
Tue 21:00 Convex Regularization in Monte-Carlo Tree Search
Tuan Q Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen
Poster
Tue 21:00 From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
Julien Perolat, Remi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro Ortega, Neil Burch, Thomas Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls
Poster
Tue 21:00 Discovering symbolic policies with deep reinforcement learning
Mikel Landajuela Larma, Brenden Petersen, Sookyung Kim, Claudio Santiago, Ruben Glatt, Nathan Mundhenk, Jacob Pettit, Daniel Faissol
Poster
Tue 21:00 An Identifiable Double VAE For Disentangled Representations
Graziano Mita, Maurizio Filippone, Pietro Michiardi
Poster
Tue 21:00 Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Bahar Taskesen, Man Chung Yue, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen
Spotlight
Wed 5:40 On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
Shunta Akiyama, Taiji Suzuki
Spotlight
Wed 6:30 How Do Adam and Training Strategies Help BNNs Optimization
Zechun Liu, Zhiqiang Shen, Shichao Li, Koen Helwegen, Dong Huang, Kwang-Ting Cheng
Spotlight
Wed 6:30 Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
Avik Pal, Yingbo Ma, Viral Shah, Christopher Rackauckas
Oral
Wed 7:00 Inferring serial correlation with dynamic backgrounds
Song Wei, Yao Xie, Dobromir Rahnev
Poster
Wed 9:00 How Do Adam and Training Strategies Help BNNs Optimization
Zechun Liu, Zhiqiang Shen, Shichao Li, Koen Helwegen, Dong Huang, Kwang-Ting Cheng
Poster
Wed 9:00 Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
Avik Pal, Yingbo Ma, Viral Shah, Christopher Rackauckas
Poster
Wed 9:00 On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
Shunta Akiyama, Taiji Suzuki
Poster
Wed 9:00 Inferring serial correlation with dynamic backgrounds
Song Wei, Yao Xie, Dobromir Rahnev
Spotlight
Wed 17:30 Selfish Sparse RNN Training
Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy
Spotlight
Wed 17:35 How Important is the Train-Validation Split in Meta-Learning?
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason Lee, Sham Kakade, Huan Wang, Caiming Xiong
Spotlight
Wed 17:35 Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
Stanislaw Jastrzebski, Devansh Arpit, Oliver Astrand, Giancarlo Kerg, Huan Wang, Caiming Xiong, Richard Socher, Kyunghyun Cho, Krzysztof J Geras
Spotlight
Wed 17:35 A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions
Gabriel Mel, Surya Ganguli
Spotlight
Wed 18:35 On the Explicit Role of Initialization on the Convergence and Implicit Bias of Overparametrized Linear Networks
Hancheng Min, Salma Tarmoun, Rene Vidal, Enrique Mallada
Oral
Wed 19:00 Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?
Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville
Spotlight
Wed 19:20 Training Recurrent Neural Networks via Forward Propagation Through Time
Anil Kag, Venkatesh Saligrama
Spotlight
Wed 19:20 Lower-Bounded Proper Losses for Weakly Supervised Classification
Shuhei M Yoshida, Takashi Takenouchi, Masashi Sugiyama
Spotlight
Wed 19:45 On the Inherent Regularization Effects of Noise Injection During Training
Oussama Dhifallah, Yue Lu
Poster
Wed 21:00 On the Inherent Regularization Effects of Noise Injection During Training
Oussama Dhifallah, Yue Lu
Poster
Wed 21:00 Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?
Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville
Poster
Wed 21:00 Breaking the Deadly Triad with a Target Network
Shangtong Zhang, Hengshuai Yao, Shimon Whiteson
Poster
Wed 21:00 Lower-Bounded Proper Losses for Weakly Supervised Classification
Shuhei M Yoshida, Takashi Takenouchi, Masashi Sugiyama
Poster
Wed 21:00 Selfish Sparse RNN Training
Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy
Poster
Wed 21:00 How Important is the Train-Validation Split in Meta-Learning?
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason Lee, Sham Kakade, Huan Wang, Caiming Xiong
Poster
Wed 21:00 Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
Stanislaw Jastrzebski, Devansh Arpit, Oliver Astrand, Giancarlo Kerg, Huan Wang, Caiming Xiong, Richard Socher, Kyunghyun Cho, Krzysztof J Geras
Poster
Wed 21:00 On the Explicit Role of Initialization on the Convergence and Implicit Bias of Overparametrized Linear Networks
Hancheng Min, Salma Tarmoun, Rene Vidal, Enrique Mallada
Poster
Wed 21:00 A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions
Gabriel Mel, Surya Ganguli
Poster
Wed 21:00 Training Recurrent Neural Networks via Forward Propagation Through Time
Anil Kag, Venkatesh Saligrama
Spotlight
Thu 5:25 PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause
Spotlight
Thu 5:40 Implicit Regularization in Tensor Factorization
Noam Razin, Asaf Maman, Nadav Cohen
Oral
Thu 6:00 Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
Yivan Zhang, Gang Niu, Masashi Sugiyama
Oral
Thu 6:00 Improved, Deterministic Smoothing for L_1 Certified Robustness
Alexander Levine, Soheil Feizi
Spotlight
Thu 7:25 Pointwise Binary Classification with Pairwise Confidence Comparisons
Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama
Poster
Thu 9:00 Implicit Regularization in Tensor Factorization
Noam Razin, Asaf Maman, Nadav Cohen
Poster
Thu 9:00 Improved, Deterministic Smoothing for L_1 Certified Robustness
Alexander Levine, Soheil Feizi
Poster
Thu 9:00 Pointwise Binary Classification with Pairwise Confidence Comparisons
Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama
Poster
Thu 9:00 Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
Yivan Zhang, Gang Niu, Masashi Sugiyama
Poster
Thu 9:00 PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause
Spotlight
Thu 17:25 REPAINT: Knowledge Transfer in Deep Reinforcement Learning
Yunzhe Tao, Sahika Genc, Jonathan Chung, TAO SUN, Sunil Mallya
Spotlight
Thu 17:35 Towards Better Robust Generalization with Shift Consistency Regularization
Shufei Zhang, Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Rui Zhang, Xinping Yi
Spotlight
Thu 18:20 Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification
Bo Pang, Ying Nian Wu
Spotlight
Thu 18:25 Breaking the Deadly Triad with a Target Network
Shangtong Zhang, Hengshuai Yao, Shimon Whiteson
Spotlight
Thu 19:35 Improving Gradient Regularization using Complex-Valued Neural Networks
Eric Yeats, Yiran Chen, Hai Li
Spotlight
Thu 20:30 Conditional Temporal Neural Processes with Covariance Loss
Boseon Yoo, Jiwoo Lee, Janghoon Ju, Seijun Chung, Soyeon Kim, Jaesik Choi
Spotlight
Thu 20:40 Regularizing towards Causal Invariance: Linear Models with Proxies
Mike Oberst, Nikolaj Thams, Jonas Peters, David Sontag
Spotlight
Thu 20:45 Overcoming Catastrophic Forgetting by Bayesian Generative Regularization
Patrick Chen Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai
Spotlight
Thu 20:50 Bayesian Structural Adaptation for Continual Learning
Abhishek Kumar, Sunabha Chatterjee, Piyush Rai
Poster
Thu 21:00 Bayesian Structural Adaptation for Continual Learning
Abhishek Kumar, Sunabha Chatterjee, Piyush Rai
Poster
Thu 21:00 Conditional Temporal Neural Processes with Covariance Loss
Boseon Yoo, Jiwoo Lee, Janghoon Ju, Seijun Chung, Soyeon Kim, Jaesik Choi
Poster
Thu 21:00 Regularizing towards Causal Invariance: Linear Models with Proxies
Mike Oberst, Nikolaj Thams, Jonas Peters, David Sontag
Poster
Thu 21:00 Overcoming Catastrophic Forgetting by Bayesian Generative Regularization
Patrick Chen Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai
Poster
Thu 21:00 Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification
Bo Pang, Ying Nian Wu
Poster
Thu 21:00 REPAINT: Knowledge Transfer in Deep Reinforcement Learning
Yunzhe Tao, Sahika Genc, Jonathan Chung, TAO SUN, Sunil Mallya
Poster
Thu 21:00 Towards Better Robust Generalization with Shift Consistency Regularization
Shufei Zhang, Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Rui Zhang, Xinping Yi
Poster
Thu 21:00 Improving Gradient Regularization using Complex-Valued Neural Networks
Eric Yeats, Yiran Chen, Hai Li
Workshop
Fri 11:45 Improving Robustness to Distribution Shifts: Methods and Benchmarks
Shiori Sagawa
Workshop
Sat 7:00 Beyond first-order methods in machine learning systems
Albert S Berahas, Tasos Kyrillidis, Fred Roosta, Amir Gholaminejad, Michael Mahoney, Rachael Tappenden, Raghu Bollapragada, Rixon Crane, J. Lyle Kim
Workshop
Sat 7:10 Recent trends in regularization methods with adaptive accuracy requirements
Stefania Bellavia
Workshop
Sat 9:30 Invited Speaker: Christian Kroer: Recent Advances in Iterative Methods for Large-Scale Game Solving
Christian Kroer
Workshop
Sat 10:35 Contributed Talk #6
Jihoon Tack
Workshop
Sat 11:10 Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks
Manuela Girotti
Workshop
Sat 12:30 Overparametrization: Insights from solvable models
Lenka Zdeborova
Workshop
Sat 12:52 Continual Learning via Function-Space Variational Inference: A Unifying View
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee-Whye Teh, Yarin Gal
Workshop
Sat 13:25 The generalization behavior of random feature and neural tangent models
Andrea Montanari
Workshop
Sat 15:35 Implicit Regularization in Overparameterized Bilevel Optimization
Paul Vicol
Workshop
Sat 15:45 Stochastic Variance-Reduced High-order Optimization for Nonconvex Optimization
Quanquan Gu
Workshop
Sat 16:50 Function space view of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm
Suriya Gunasekar
Workshop
Sat 17:00 Afternoon Poster Session: DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting
Cristian Challu
Workshop
Sat 17:45 Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Aviral Kumar, Rishabh Agarwal, Aaron Courville, Tengyu Ma, George Tucker, Sergey Levine
Workshop
Sat 18:00 Beyond Implicit Regularization: Avoiding Overfitting via Regularizer Mirror Descent
Navid Azizan, Sahin Lale, Babak Hassibi
Workshop
Fast Certified Robust Training with Short Warmup
Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh
Workshop
Consistency Regularization for Adversarial Robustness
Jihoon Tack, Sihyun Yu, Jongheon Jeong, Minseon Kim, Sung Ju Hwang, Jinwoo Shin
Workshop
Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations
Ziquan Liu, Yufei Cui, Antoni Chan
Workshop
On Alignment in Deep Linear Neural Networks
Adit Radhakrishnan, Eshaan Nichani, Daniel Bernstein, Caroline Uhler
Workshop
Label Noise SGD Provably Prefers Flat Global Minimizers
Alex Damian, Tengyu Ma, Jason Lee
Workshop
Sample Complexity and Overparameterization Bounds for Temporal Difference Learning with Neural Network Approximation
Semih Cayci, Siddhartha Satpathi, Niao He, R Srikant
Workshop
Double Descent in Feature Selection: Revisiting LASSO and Basis Pursuit
David Bosch, Ashkan Panahi, Ayca Ozcelikkale
Workshop
Gradient Starvation: A Learning Proclivity in Neural Networks
Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio, Aaron Courville, Doina Precup, Guillaume Lajoie
Workshop
Implicit Greedy Rank Learning in Autoencoders via Overparameterized Linear Networks
Shih-Yu Sun, Vimal Thilak, Etai Littwin, Omid Saremi, Josh M Susskind
Workshop
Adversarially Robust Learning via Entropic Regularization
Gauri Jagatap, Ameya Joshi, Animesh Chowdhury, Siddharth Garg, Chinmay Hegde
Workshop
Surprising benefits of ridge regularization for noiseless regression
Konstantin Donhauser, Alexandru Tifrea, Michael Aerni, Reinhard Heckel, Fanny Yang
Workshop
Maximizing the robust margin provably overfits on noiseless data
Fanny Yang, Reinhard Heckel, Michael Aerni, Alexandru Tifrea, Konstantin Donhauser
Workshop
Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting and Regularization
Ke Wang, Christos Thrampoulidis
Workshop
Consistency Regularization for Training Confidence-Calibrated Classifiers
Workshop
Consistency Regularization Can Improve Robustness to Label Noise
Workshop
Augmented Invariant Regularization
Workshop
Towards Principled Disentanglement for Domain Generalization
Hanlin Zhang, Yi-Fan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric Xing
Workshop
Regularization and False Alarms Quantification: Towards an Approach to Assess the Economic Value of Machine Learning
Nima Safaei, Pooria Assadi
Workshop
Novel disease detection using ensembles with regularized disagreement
Alexandru Tifrea, Eric Stavarache, Fanny Yang
Workshop
Finding the Near Optimal Policy via Reductive Regularization in MDPs
Wenhao Yang, Xiang Li, Guangzeng Xie, Zhihua Zhang
Workshop
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity
Kaiqing Zhang, Xiangyuan Zhang, Bin Hu, Tamer Basar
Workshop
Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning
Sarah Rathnam
Workshop
Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation
Semih Cayci, Niao He, R Srikant
Workshop
Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation
Yue Guan, Qifan Zhang, Panagiotis Tsiotras
Workshop
Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Aviral Kumar, Rishabh Agarwal, Aaron Courville, Tengyu Ma, George Tucker, Sergey Levine
Workshop
SparseDice: Imitation Learning for Temporally Sparse Data via Regularization
Alberto Camacho, Izzeddin Gur, Marcin Moczulski, Ofir Nachum, Aleksandra Faust
Workshop
Visualizing MuZero Models
joery de Vries, Ken Voskuil, Thomas M Moerland, Aske Plaat
Workshop
Semi-supervised Deconvolution of Spatial Transcriptomics in Breast Tumors
xueer chen
Workshop
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations
Yuping Luo, Tengyu Ma
Workshop
Coordinate-wise Control Variates for Deep Policy Gradients
Yuanyi Zhong, Yuan Zhou, Jian Peng
Workshop
Disentangled Attention as Intrinsic Regularization for Bimanual Multi-Object Manipulation
Minghao Zhang, Pingcheng Jian, Yi Wu, Harry (Huazhe) Xu, Xiaolong Wang
Workshop
Offline Reinforcement Learning as Anti-Exploration
Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist
Workshop
Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Aviral Kumar, Rishabh Agarwal, Aaron Courville, Tengyu Ma, George Tucker, Sergey Levine
Workshop
Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA
Sebastien Lachapelle, Pau Rodriguez, Remi Le Priol, Alexandre Lacoste
Workshop
Continual Learning via Function-Space Variational Inference: A Unifying View
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee-Whye Teh, Yarin Gal
Workshop
Beyond Implicit Regularization: Avoiding Overfitting via Regularizer Mirror Descent
Navid Azizan, Sahin Lale, Babak Hassibi
Workshop
Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Aviral Kumar, Rishabh Agarwal, Aaron Courville, Tengyu Ma, George Tucker, Sergey Levine