← Authors

Yunhao Tang

45 papers · 2018–2025 · 8 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (4) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (8) πŸƒ Academic Marathon (7) πŸ—ΊοΈ Taxonomy Completionist (10) 🌈 Renaissance Researcher (5) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🏠 Conference Loyalist (24) 🀝 Dynamic Duo (23) πŸ‘‘ Triple Crown πŸ† Keyword Champion (4) πŸ† Grand Slam πŸ”¬ Deep Specialist (23) πŸ’Ž Century Club (45) ⚑ Prolific Year (8) πŸ—ƒοΈ Keyword Collector (154) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (8)

Conferences

ICML (24) AISTATS (8) NIPS (7) AAAI (2) CORL (1) ICLR (1) IJCAI (1) JMLR (1)

Top co-authors

RΓ©mi Munos (23) Mark Rowland (19) Michal Valko (17) Will Dabney (10) Bernardo Avila Pires (7) Krzysztof Choromanski (7) Daniele Calandriello (6) Zhaohan Daniel Guo (6) Aldo Pacchiano (6) Bilal Piot (6)

Keywords

reinforcement learning (17) deep reinforcement learning (8) policy optimization (8) distributional reinforcement learning (6) variance reduction (6) policy gradient (6) off-policy learning (5) representation learning (4) value function (3) evolution strategy (3) blackbox optimization (3) multi-step learning (3) value estimation (2) off-policy evaluation (2) policy evaluation (2) spectral decomposition (2) gradient estimation (2) continuous control (2) self-supervised learning (2) sample complexity (2)

Papers

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