Yunhao Tang - RankMe

106 papers A* 35A 9Misc 1Journal 60

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. AISTATS Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L. Borsa, Arthur Guez, Will Dabney
2025	J	`jnl`	Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards. CoRR Charles Arnal, Gaëtan Narozniak, Vivien Cabannes, Yunhao Tang, Julia Kempe, Rémi Munos
2025	A*	`conf`	Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics. ICML Tyler Kastner, Mark Rowland, Yunhao Tang, Murat A. Erdogdu, Amir-massoud Farahmand
2025	J	`jnl`	Learning to chain-of-thought with Jensen's evidence lower bound. CoRR Yunhao Tang, Sid Wang, Rémi Munos
2025	J	`jnl`	LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Training. CoRR Bo Wu, Sid Wang, Yunhao Tang, Jia Ding, Eryk Helenowski, Liang Tan, Tengyu Xu, Tushar Gowda, Zhengxing Chen, Chen Zhu, Xiaocheng Tang, Yundi Qian, Beibei Zhu, Rui Hou
2025	J	`jnl`	On a few pitfalls in KL divergence gradient estimation for RL. CoRR Yunhao Tang, Rémi Munos
2025	A*	`conf`	Optimizing Language Models for Inference Time Objectives using Reinforcement Learning. ICML Yunhao Tang, Kunhao Zheng, Gabriel Synnaeve, Rémi Munos
2025	J	`jnl`	Optimizing Language Models for Inference Time Objectives using Reinforcement Learning. CoRR Yunhao Tang, Kunhao Zheng, Gabriel Synnaeve, Rémi Munos
2025	J	`jnl`	RL-finetuning LLMs from on- and off-policy data with a single algorithm. CoRR Yunhao Tang, Taco Cohen, David W. Zhang, Michal Valko, Rémi Munos
2025	J	`jnl`	Soft Policy Optimization: Online Off-Policy RL for Sequence Models. CoRR Taco Cohen, David W. Zhang, Kunhao Zheng, Yunhao Tang, Rémi Munos, Gabriel Synnaeve
2024	A*	`conf`	A Distributional Analogue to the Successor Representation. ICML Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
2024	J	`jnl`	A Distributional Analogue to the Successor Representation. CoRR Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
2024	J	`jnl`	A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. CoRR Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana Borsa, Arthur Guez, Will Dabney
2024	J	`jnl`	An Analysis of Quantile Temporal-Difference Learning. J. Mach. Learn. Res. Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney
2024	A*	`conf`	Generalized Preference Optimization: A Unified Approach to Offline Alignment. ICML Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
2024	J	`jnl`	Generalized Preference Optimization: A Unified Approach to Offline Alignment. CoRR Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
2024	A*	`conf`	Human Alignment of Large Language Models through Online Preference Optimisation. ICML Daniele Calandriello, Zhaohan 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
2024	J	`jnl`	Human Alignment of Large Language Models through Online Preference Optimisation. CoRR 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
2024	A*	`conf`	Learning Uncertainty-Aware Temporally-Extended Actions. AAAI Joongkyu Lee, Seung Joon Park, Yunhao Tang, Min-hwan Oh
2024	J	`jnl`	Learning Uncertainty-Aware Temporally-Extended Actions. CoRR Joongkyu Lee, Seung Joon Park, Yunhao Tang, Min-hwan Oh
2024	A*	`conf`	Nash Learning from Human Feedback. ICML Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Côme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot
2024	A*	`conf`	Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. NeurIPS Mark Rowland, Kevin Kevin Li, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
2024	J	`jnl`	Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
2024	J	`jnl`	Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney
2024	J	`jnl`	Offline Regularised Reinforcement Learning for Large Language Models Alignment. CoRR 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
2024	A*	`conf`	On scalable oversight with weak LLMs judging strong LLMs. NeurIPS 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
2024	J	`jnl`	On scalable oversight with weak LLMs judging strong LLMs. CoRR 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
2024	J	`jnl`	Understanding the performance gap between online and offline alignment algorithms. CoRR Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Yuan Cao, Eugene Tarassov, Rémi Munos, Bernardo Ávila Pires, Michal Valko, Yong Cheng, Will Dabney
2023	J	`jnl`	An Analysis of Quantile Temporal-Difference Learning. CoRR Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney
2023	A*	`conf`	DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. ICML Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
2023	J	`jnl`	DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. CoRR Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
2023	A*	`conf`	Fast Rates for Maximum Entropy Exploration. ICML Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	J	`jnl`	Fast Rates for Maximum Entropy Exploration. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	J	`jnl`	Nash Learning from Human Feedback. CoRR 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
2023	A*	`conf`	Quantile Credit Assignment. ICML 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
2023	A*	`conf`	Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML 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
2023	J	`jnl`	Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR 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
2023	A*	`conf`	Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. ICML Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
2023	J	`jnl`	Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. CoRR Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
2023	A*	`conf`	The Edge of Orthogonality: A Simple View of What Makes BYOL Tick. ICML Pierre Harvey Richemond, Allison C. Tam, Yunhao Tang, Florian Strub, Bilal Piot, Felix Hill
2023	J	`jnl`	The Edge of Orthogonality: A Simple View of What Makes BYOL Tick. CoRR Pierre H. Richemond, Allison C. Tam, Yunhao Tang, Florian Strub, Bilal Piot, Felix Hill
2023	A*	`conf`	The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. ICML Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. CoRR Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	Towards a Better Understanding of Representation Dynamics under TD-learning. CoRR Yunhao Tang, Rémi Munos
2023	A*	`conf`	Towards a better understanding of representation dynamics under TD-learning. ICML Yunhao Tang, Rémi Munos
2023	A*	`conf`	Understanding Self-Predictive Learning for Reinforcement Learning. ICML 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
2023	A*	`conf`	VA-learning as a more efficient alternative to Q-learning. ICML Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
2023	J	`jnl`	VA-learning as a more efficient alternative to Q-learning. CoRR Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
2022	A*	`conf`	BYOL-Explore: Exploration by Bootstrapped Prediction. NeurIPS 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
2022	J	`jnl`	BYOL-Explore: Exploration by Bootstrapped Prediction. CoRR 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
2022	A*	`conf`	Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning. ICML Yunhao Tang
2022	A*	`conf`	From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. ICML Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
2022	J	`jnl`	From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. CoRR Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
2022	J	`jnl`	KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR 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
2022	A	`conf`	Marginalized Operators for Off-policy Reinforcement Learning. AISTATS Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2022	J	`jnl`	Marginalized Operators for Off-policy Reinforcement Learning. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2022	A*	`conf`	The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. NeurIPS Yunhao Tang, Rémi Munos, Mark Rowland, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
2022	J	`jnl`	The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
2022	J	`jnl`	Understanding Self-Predictive Learning for Reinforcement Learning. CoRR 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
2021	J	`jnl`	Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning. CoRR Yunhao Tang
2021	J	`jnl`	ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning. CoRR Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Daiyi Peng, Deepali Jain, Wenbo Gao, Aldo Pacchiano, Tamás Sarlós, Yuxiang Yang
2021	A	`conf`	Guiding Evolutionary Strategies with Off-Policy Actor-Critic. AAMAS Yunhao Tang
2021	A	`conf`	Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning. AISTATS Yunhao Tang, Alp Kucukelbir
2021	—	`—`	Reinforcement Learning: New Algorithms and An Application for Integer Programming. Yunhao Tang
2021	A*	`conf`	Revisiting Peng's Q(λ) for Modern Reinforcement Learning. ICML Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
2021	J	`jnl`	Revisiting Peng's Q(λ) for Modern Reinforcement Learning. CoRR Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
2021	A*	`conf`	Taylor Expansion of Discount Factors. ICML Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2021	J	`jnl`	Taylor Expansion of Discount Factors. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2021	A*	`conf`	Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. NeurIPS Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
2021	J	`jnl`	Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. CoRR Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
2021	J	`jnl`	Unlocking Pixels for Reinforcement Learning via Implicit Attention. CoRR Krzysztof Choromanski, Deepali Jain, Jack Parker-Holder, Xingyou Song, Valerii Likhosherstov, Anirban Santara, Aldo Pacchiano, Yunhao Tang, Adrian Weller
2020	A	`conf`	Discrete Action On-Policy Learning with Action-Value Critic. AISTATS Yuguang Yue, Yunhao Tang, Mingzhang Yin, Mingyuan Zhou
2020	J	`jnl`	Discrete Action On-Policy Learning with Action-Value Critic. CoRR Yuguang Yue, Yunhao Tang, Mingzhang Yin, Mingyuan Zhou
2020	A*	`conf`	Discretizing Continuous Action Space for On-Policy Optimization. AAAI Yunhao Tang, Shipra Agrawal
2020	A*	`conf`	ES-MAML: Simple Hessian-Free Meta Learning. ICLR Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang
2020	J	`jnl`	Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning. CoRR Yunhao Tang, Alp Kucukelbir
2020	A*	`conf`	Learning to Score Behaviors for Guided Policy Optimization. ICML Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan
2020	A*	`conf`	Monte-Carlo Tree Search as Regularized Policy Optimization. ICML Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
2020	J	`jnl`	Monte-Carlo Tree Search as Regularized Policy Optimization. CoRR Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
2020	J	`jnl`	Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies. CoRR Yunhao Tang, Krzysztof Choromanski
2020	A	`conf`	Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes. AISTATS Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
2020	A*	`conf`	Reinforcement Learning for Integer Programming: Learning to Cut. ICML Yunhao Tang, Shipra Agrawal, Yuri Faenza
2020	A*	`conf`	Self-Imitation Learning via Generalized Lower Bound Q-learning. NeurIPS Yunhao Tang
2020	J	`jnl`	Self-Imitation Learning via Generalized Lower Bound Q-learning. CoRR Yunhao Tang
2020	A*	`conf`	Taylor Expansion Policy Optimization. ICML Yunhao Tang, Michal Valko, Rémi Munos
2020	J	`jnl`	Taylor Expansion Policy Optimization. CoRR Yunhao Tang, Michal Valko, Rémi Munos
2020	A	`conf`	Variance Reduction for Evolution Strategies via Structured Control Variates. AISTATS Yunhao Tang, Krzysztof Choromanski, Alp Kucukelbir
2019	J	`jnl`	Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces. CoRR Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
2019	J	`jnl`	Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy. CoRR Yunhao Tang, Mingzhang Yin, Mingyuan Zhou
2019	J	`jnl`	Discretizing Continuous Action Space for On-Policy Optimization. CoRR Yunhao Tang, Shipra Agrawal
2019	J	`jnl`	ES-MAML: Simple Hessian-Free Meta Learning. CoRR Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang
2019	A*	`conf`	From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization. NeurIPS Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani
2019	A	`conf`	KAMA-NNs: Low-dimensional Rotation Based Neural Networks. AISTATS Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang
2019	A	`conf`	Orthogonal Estimation of Wasserstein Distances. AISTATS Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, Adrian Weller
2019	J	`jnl`	Orthogonal Estimation of Wasserstein Distances. CoRR Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, Adrian Weller
2019	Misc	`conf`	Provably Robust Blackbox Optimization for Reinforcement Learning. CoRL Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani
2019	J	`jnl`	Reinforcement Learning for Integer Programming: Learning to Cut. CoRR Yunhao Tang, Shipra Agrawal, Yuri Faenza
2019	J	`jnl`	Reinforcement Learning with Chromatic Networks. CoRR Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Wenbo Gao, Aldo Pacchiano, Tamás Sarlós, Deepali Jain, Yuxiang Yang
2019	J	`jnl`	Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes. CoRR Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
2019	J	`jnl`	Variance Reduction for Evolution Strategies via Structured Control Variates. CoRR Yunhao Tang, Krzysztof Choromanski, Alp Kucukelbir
2019	J	`jnl`	Wasserstein Reinforcement Learning. CoRR Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael I. Jordan
2018	J	`jnl`	Boosting Trust Region Policy Optimization by Normalizing Flows Policy. CoRR Yunhao Tang, Shipra Agrawal
2018	A*	`conf`	Exploration by Distributional Reinforcement Learning. IJCAI Yunhao Tang, Shipra Agrawal
2018	J	`jnl`	Exploration by Distributional Reinforcement Learning. CoRR Yunhao Tang, Shipra Agrawal
2018	J	`jnl`	Implicit Policy for Reinforcement Learning. CoRR Yunhao Tang, Shipra Agrawal
2017	J	`jnl`	Variational Deep Q Network. CoRR Yunhao Tang, Alp Kucukelbir