Yunhao Tang

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 88

Top Topics

Generative Model

Reinforcement Learning

Variance Reduction

Monte Carlo Sampling

Top Venues

Publications

Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana Borsa, Arthur Guez, Will Dabney
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. CoRR (2024)
Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
A Distributional Analogue to the Successor Representation. CoRR (2024)
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Yuan Cao, Eugene Tarassov, Rémi Munos, Bernardo Ávila Pires, Michal Valko, Yong Cheng, Will Dabney
Understanding the performance gap between online and offline alignment algorithms. CoRR (2024)
Joongkyu Lee, Seung Joon Park, Yunhao Tang, Min-hwan Oh
Learning Uncertainty-Aware Temporally-Extended Actions. CoRR (2024)
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
Generalized Preference Optimization: A Unified Approach to Offline Alignment. CoRR (2024)
Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney
Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. CoRR (2024)
Daniele Calandriello, Daniel Guo, Rémi Munos, Mark Rowland, Yunhao Tang, Bernardo Ávila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot
Human Alignment of Large Language Models through Online Preference Optimisation. CoRR (2024)
Joongkyu Lee, Seung Joon Park, Yunhao Tang, Min-hwan Oh
Learning Uncertainty-Aware Temporally-Extended Actions. AAAI (2024)
Zachary Kenton, Noah Y. Siegel, János Kramár, Jonah Brown-Cohen, Samuel Albanie, Jannis Bulian, Rishabh Agarwal, David Lindner, Yunhao Tang, Noah D. Goodman, Rohin Shah
On scalable oversight with weak LLMs judging strong LLMs. CoRR (2024)
Pierre Harvey Richemond, Yunhao Tang, Daniel Guo, Daniele Calandriello, Mohammad Gheshlaghi Azar, Rafael Rafailov, Bernardo Ávila Pires, Eugene Tarassov, Lucas Spangher, Will Ellsworth, Aliaksei Severyn, Jonathan Mallinson, Lior Shani, Gil Shamir, Rishabh Joshi, Tianqi Liu, Rémi Munos, Bilal Piot
Offline Regularised Reinforcement Learning for Large Language Models Alignment. CoRR (2024)
Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR (2024)
Yunhao Tang, Rémi Munos
Towards a Better Understanding of Representation Dynamics under TD-learning. CoRR (2023)
Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot
Nash Learning from Human Feedback. CoRR (2023)
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. CoRR (2023)
Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
VA-learning as a more efficient alternative to Q-learning. CoRR (2023)
Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. CoRR (2023)
Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
VA-learning as a more efficient alternative to Q-learning. ICML (2023)
Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. ICML (2023)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. ICML (2023)
Pierre Harvey Richemond, Allison C. Tam, Yunhao Tang, Florian Strub, Bilal Piot, Felix Hill
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick. ICML (2023)
Yunhao Tang, Rémi Munos
Towards a better understanding of representation dynamics under TD-learning. ICML (2023)
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
Understanding Self-Predictive Learning for Reinforcement Learning. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
Fast Rates for Maximum Entropy Exploration. CoRR (2023)
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. ICML (2023)
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR (2023)
Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney
An Analysis of Quantile Temporal-Difference Learning. CoRR (2023)
Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Rémi Munos
Quantile Credit Assignment. ICML (2023)
Pierre H. Richemond, Allison C. Tam, Yunhao Tang, Florian Strub, Bilal Piot, Felix Hill
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick. CoRR (2023)
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
Fast Rates for Maximum Entropy Exploration. ICML (2023)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. CoRR (2023)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Marginalized Operators for Off-policy Reinforcement Learning. CoRR (2022)
Zhaohan Guo, Shantanu Thakoor, Miruna Pislar, Bernardo Ávila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot
BYOL-Explore: Exploration by Bootstrapped Prediction. NeurIPS (2022)
Yunhao Tang, Rémi Munos, Mark Rowland, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. NeurIPS (2022)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Marginalized Operators for Off-policy Reinforcement Learning. AISTATS (2022)
Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pislar, Bernardo Ávila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot
BYOL-Explore: Exploration by Bootstrapped Prediction. CoRR (2022)
Tadashi Kozuno, Wenhao Yang, Nino Vieillard, Toshinori Kitamura, Yunhao Tang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Michal Valko, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR (2022)
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
Understanding Self-Predictive Learning for Reinforcement Learning. CoRR (2022)
Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. CoRR (2022)
Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. CoRR (2022)
Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. ICML (2022)
Yunhao Tang
Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning. ICML (2022)
Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Daiyi Peng, Deepali Jain, Wenbo Gao, Aldo Pacchiano, Tamás Sarlós, Yuxiang Yang
ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning. CoRR (2021)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. NeurIPS (2021)
Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
Revisiting Peng's Q(λ) for Modern Reinforcement Learning. CoRR (2021)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Taylor Expansion of Discount Factors. ICML (2021)
Yunhao Tang
Guiding Evolutionary Strategies with Off-Policy Actor-Critic. AAMAS (2021)
Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
Revisiting Peng's Q(λ) for Modern Reinforcement Learning. ICML (2021)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. CoRR (2021)
Yunhao Tang
Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning. CoRR (2021)
Krzysztof Choromanski, Deepali Jain, Jack Parker-Holder, Xingyou Song, Valerii Likhosherstov, Anirban Santara, Aldo Pacchiano, Yunhao Tang, Adrian Weller
Unlocking Pixels for Reinforcement Learning via Implicit Attention. CoRR (2021)
Yunhao Tang, Alp Kucukelbir
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning. AISTATS (2021)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Taylor Expansion of Discount Factors. CoRR (2021)
Yuguang Yue, Yunhao Tang, Mingzhang Yin, Mingyuan Yin
Discrete Action On-Policy Learning with Action-Value Critic. CoRR (2020)
Yunhao Tang, Krzysztof Choromanski
Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies. CoRR (2020)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes. AISTATS (2020)
Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang
ES-MAML: Simple Hessian-Free Meta Learning. ICLR (2020)
Yuguang Yue, Yunhao Tang, Mingzhang Yin, Mingyuan Zhou
Discrete Action On-Policy Learning with Action-Value Critic. AISTATS (2020)
Yunhao Tang, Michal Valko, Rémi Munos
Taylor Expansion Policy Optimization. CoRR (2020)
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan
Learning to Score Behaviors for Guided Policy Optimization. ICML (2020)
Yunhao Tang, Krzysztof Choromanski, Alp Kucukelbir
Variance Reduction for Evolution Strategies via Structured Control Variates. AISTATS (2020)
Yunhao Tang, Alp Kucukelbir
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning. CoRR (2020)
Yunhao Tang, Shipra Agrawal
Discretizing Continuous Action Space for On-Policy Optimization. AAAI (2020)
Yunhao Tang, Michal Valko, Rémi Munos
Taylor Expansion Policy Optimization. ICML (2020)
Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
Monte-Carlo Tree Search as Regularized Policy Optimization. CoRR (2020)
Yunhao Tang, Shipra Agrawal, Yuri Faenza
Reinforcement Learning for Integer Programming: Learning to Cut. ICML (2020)
Yunhao Tang
Self-Imitation Learning via Generalized Lower Bound Q-learning. CoRR (2020)
Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
Monte-Carlo Tree Search as Regularized Policy Optimization. ICML (2020)
Yunhao Tang
Self-Imitation Learning via Generalized Lower Bound Q-learning. NeurIPS (2020)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces. CoRR (2019)
Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang
KAMA-NNs: Low-dimensional Rotation Based Neural Networks. AISTATS (2019)
Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, Adrian Weller
Orthogonal Estimation of Wasserstein Distances. CoRR (2019)
Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang
ES-MAML: Simple Hessian-Free Meta Learning. CoRR (2019)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani
Provably Robust Blackbox Optimization for Reinforcement Learning. CoRL (2019)
Yunhao Tang, Mingzhang Yin, Mingyuan Zhou
Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy. CoRR (2019)
Yunhao Tang, Shipra Agrawal, Yuri Faenza
Reinforcement Learning for Integer Programming: Learning to Cut. CoRR (2019)
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael I. Jordan
Wasserstein Reinforcement Learning. CoRR (2019)
Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, Adrian Weller
Orthogonal Estimation of Wasserstein Distances. AISTATS (2019)
Yunhao Tang, Shipra Agrawal
Discretizing Continuous Action Space for On-Policy Optimization. CoRR (2019)
Yunhao Tang, Krzysztof Choromanski, Alp Kucukelbir
Variance Reduction for Evolution Strategies via Structured Control Variates. CoRR (2019)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization. NeurIPS (2019)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes. CoRR (2019)
Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Wenbo Gao, Aldo Pacchiano, Tamás Sarlós, Deepali Jain, Yuxiang Yang
Reinforcement Learning with Chromatic Networks. CoRR (2019)
Yunhao Tang, Shipra Agrawal
Exploration by Distributional Reinforcement Learning. CoRR (2018)
Yunhao Tang, Shipra Agrawal
Exploration by Distributional Reinforcement Learning. IJCAI (2018)
Yunhao Tang, Shipra Agrawal
Implicit Policy for Reinforcement Learning. CoRR (2018)
Yunhao Tang, Shipra Agrawal
Boosting Trust Region Policy Optimization by Normalizing Flows Policy. CoRR (2018)
Yunhao Tang, Alp Kucukelbir
Variational Deep Q Network. CoRR (2017)