← Authors

Jiafan He

24 papers · 2019–2025 · 8 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (8) πŸƒ Academic Marathon (6) 🐝 Cross-Pollinator (7) πŸ—ΊοΈ Taxonomy Completionist (27) πŸ† Keyword Champion (2) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (12) 🀝 Dynamic Duo (23) πŸ—ƒοΈ Keyword Collector (70) πŸ”₯ Unstoppable (5) πŸ’Ž Century Club (24) ⚑ Prolific Year (5)

Conferences

ICML (11) NIPS (6) ICLR (2) ACML (1) AISTATS (1) COLT (1) IJCAI (1) UAI (1)

Top co-authors

Quanquan Gu (23) Dongruo Zhou (10) Heyang Zhao (5) Yifei Min (3) Tianhao Wang (3) Qiwei Di (3) Tong Zhang (3) Weitong ZHANG (3) Zhiyuan Fan (2) David Zeng (1)

Keywords

regret bound (15) reinforcement learning (7) linear function approximation (6) function approximation (5) markov decision process (4) linear bandit (3) minimax optimal (3) contextual bandit (3) linear mixture mdp (3) linear contextual bandit (2) online learning (2) minimax optimal regret (2) linear markov decision process (2) sample complexity (2) online algorithm (2) stochastic shortest path (2) eluder dimension (2) value iteration (2) upper confidence bound (2) asynchronous communication (2)

Papers

Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback ICML 2025
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs ICLR 2024
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning ICLR 2024
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption ICML 2024
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation NIPS 2024
Achieving Constant Regret in Linear Markov Decision Processes NIPS 2024
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits ICML 2023
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension UAI 2023
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency COLT 2023
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path ICML 2023
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes ICML 2023
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation ICML 2023
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits ICML 2023
On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs ICML 2022
Learning Stochastic Shortest Path with Linear Function Approximation ICML 2022
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs AISTATS 2022
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes ACML 2022
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits NIPS 2022
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions NIPS 2022
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping ICML 2021
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs NIPS 2021
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation ICML 2021
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation NIPS 2021
Achieving a Fairer Future by Changing the Past IJCAI 2019