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Dylan J. Foster — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Dylan J. Foster
24
papers ·
71
citations ·
18
h-index
Microsoft (United States) · Microsoft Research (United Kingdom)
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Most-cited papers
Independent Policy Gradient Methods for Competitive Reinforcement Learning
2021 · 22 citations
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
2020 · 21 citations
Model selection for contextual bandits
2019 · 10 citations
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
2021 · 3 citations
Interaction-Grounded Learning with Action-inclusive Feedback
2022 · 2 citations
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
2022 · 2 citations
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient
2023 · 2 citations
Can large language models explore in-context?
2024 · 2 citations
The Role of Coverage in Online Reinforcement Learning
2022 · 1 citations
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring
2023 · 1 citations
Scalable Online Exploration via Coverability
2024 · 1 citations
The Power of Resets in Online Reinforcement Learning
2024 · 1 citations
Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF
2024 · 1 citations
Representation-Based Exploration for Language Models: From Test-Time to Post-Training
2025
Necessary and Sufficient Oracles: Toward a Computational Taxonomy For Reinforcement Learning
2025
Top co-authors
Akshay Krishnamurthy
· 9
Alexander Rakhlin
· 7
Noah Golowich
· 5
Philip Amortila
· 3
Tengyang Xie
· 3
Zakaria Mhammedi
· 3
Ayush Sekhari
· 2
John Langford
· 2
Nan Jiang
· 2
Nan Jiang
· 2
Sham M. Kakade
· 2
Yunzong Xu
· 2
Topics
Exploration
Model-Based RL
Value-Based
RLHF & Alignment
Multi-Agent
Game AI
Offline RL
Policy Gradient
Safe RL
Meta-RL