Yunhao Tang

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning AISTATS 2025 Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics ICML 2025 Optimizing Language Models for Inference Time Objectives using Reinforcement Learning ICML 2025 A Distributional Analogue to the Successor Representation ICML 2024 An Analysis of Quantile Temporal-Difference Learning JMLR 2024 Generalized Preference Optimization: A Unified Approach to Offline Alignment ICML 2024 Human Alignment of Large Language Models through Online Preference Optimisation ICML 2024 Learning Uncertainty-Aware Temporally-Extended Actions AAAI 2024 Nash Learning from Human Feedback ICML 2024 Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model NeurIPS 2024 On scalable oversight with weak LLMs judging strong LLMs NeurIPS 2024 DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm ICML 2023 Fast Rates for Maximum Entropy Exploration ICML 2023 Quantile Credit Assignment ICML 2023 Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice ICML 2023 Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition ICML 2023 The Edge of Orthogonality: A Simple View of What Makes BYOL Tick ICML 2023 The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation ICML 2023 Towards a better understanding of representation dynamics under TD-learning ICML 2023 Understanding Self-Predictive Learning for Reinforcement Learning ICML 2023 VA-learning as a more efficient alternative to Q-learning ICML 2023 Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning ICML 2022 BYOL-Explore: Exploration by Bootstrapped Prediction NeurIPS 2022 From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses ICML 2022 Marginalized Operators for Off-policy Reinforcement Learning AISTATS 2022 The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning NeurIPS 2022 Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning AISTATS 2021 Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning ICML 2021 Taylor Expansion of Discount Factors ICML 2021 Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation NeurIPS 2021 Discrete Action On-Policy Learning with Action-Value Critic AISTATS 2020 Discretizing Continuous Action Space for On-Policy Optimization AAAI 2020 ES-MAML: Simple Hessian-Free Meta Learning ICLR 2020 Learning to Score Behaviors for Guided Policy Optimization ICML 2020 Monte-Carlo Tree Search as Regularized Policy Optimization ICML 2020 Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes AISTATS 2020 Reinforcement Learning for Integer Programming: Learning to Cut ICML 2020 Self-Imitation Learning via Generalized Lower Bound Q-learning NeurIPS 2020 Taylor Expansion Policy Optimization ICML 2020 Variance Reduction for Evolution Strategies via Structured Control Variates AISTATS 2020 From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization NeurIPS 2019 KAMA-NNs: Low-dimensional Rotation Based Neural Networks AISTATS 2019 Orthogonal Estimation of Wasserstein Distances AISTATS 2019 Provably Robust Blackbox Optimization for Reinforcement Learning CoRL 2019 Exploration by Distributional Reinforcement Learning IJCAI 2018