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Nan Jiang — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Nan Jiang
40
papers ·
694
citations ·
38
h-index
Nanjing Agricultural University · East China Jiaotong University · Emory University · Shandong Institute of Business and Technology
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable
2016 · 153 citations
Information-Theoretic Considerations in Batch Reinforcement Learning
2019 · 72 citations
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
2019 · 68 citations
RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning
2020 · 41 citations
Repeated Inverse Reinforcement Learning
2017 · 30 citations
Batch Value-function Approximation with Only Realizability
2020 · 30 citations
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
2019 · 29 citations
Hierarchical Imitation and Reinforcement Learning
2018 · 27 citations
On Oracle-Efficient PAC RL with Rich Observations
2018 · 20 citations
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
2018 · 17 citations
Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison
2020 · 16 citations
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning
2021 · 16 citations
Bellman-consistent Pessimism for Offline Reinforcement Learning
2021 · 16 citations
Minimax Value Interval for Off-Policy Evaluation and Policy Optimization
2020 · 14 citations
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency
2021 · 14 citations
Top co-authors
Alekh Agarwal
· 6
Tengyang Xie
· 6
Akshay Krishnamurthy
· 4
John Langford
· 3
Philip Amortila
· 3
Yisong Yue
· 3
Aditya Modi
· 2
Cameron Voloshin
· 2
Csaba Szepesv\'ari
· 2
Hoang M. Le
· 2
Jinglin Chen
· 2
Masatoshi Uehara
· 2
Topics
Offline RL
Value-Based
Model-Based RL
Exploration
Policy Gradient
Meta-RL
Safe RL
RLHF & Alignment
Game AI
Multi-Agent