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← Authors

Benjamin Van Roy

37 papers · 2013–2026 · 8 conferences · across top CS/AI conferences

Achievements

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+14 more ↓ 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (12) 🌍 Conference Polyglot (7)
🌍 Conference Polyglot (7) πŸƒ Academic Marathon (11) 🌈 Renaissance Researcher (5) 🏠 Conference Loyalist (20) πŸ† Grand Slam πŸ”¬ Deep Specialist (13) 🀝 Dynamic Duo (14) πŸ† Keyword Champion (3) πŸ—ƒοΈ Keyword Collector (104) ❓ The Questioner ⚑ Prolific Year (6) πŸ“ˆ Trend Setter πŸ’Ž Century Club (36) πŸ”₯ Unstoppable (9)

Conferences

NIPS (20) ICML (7) JMLR (3) ICLR (2) UAI (2) AAAI (1) AISTATS (1) COLT (1)

Top co-authors

Ian Osband (14) Zheng Wen (11) Xiuyuan Lu (8) Vikranth Dwaracherla (6) Morteza Ibrahimi (5) Seyed Mohammad Asghari (5) Daniel Russo (4) Dilip Arumugam (3) Botao Hao (3) Shi Dong (3)

Keywords

regret bound (17) thompson sampling (11) reinforcement learning (10) multi-armed bandit (6) information theory (6) bayesian regret (6) posterior sampling (5) markov decision process (4) neural network (3) randomized value function (3) rate-distortion theory (3) optimistic exploration (3) eluder dimension (3) joint prediction (3) sequential decision making (2) online optimization (2) exploration (2) function approximation (2) bayesian inference (2) model-based reinforcement learning (2)

Papers

Misalignment from Treating Means as Ends AAAI 2026 Efficient Exploration for LLMs ICML 2024 An Information-Theoretic Analysis of In-Context Learning ICML 2024 Epistemic Neural Networks NIPS 2023 A Definition of Continual Reinforcement Learning NIPS 2023 Nonstationary Bandit Learning via Predictive Sampling AISTATS 2023 Leveraging Demonstrations to Improve Online Learning: Quality Matters ICML 2023 Approximate Thompson Sampling via Epistemic Neural Networks UAI 2023 An Information-Theoretic Framework for Deep Learning NIPS 2022 Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning NIPS 2022 Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States JMLR 2022 An Analysis of Ensemble Sampling NIPS 2022 The Neural Testbed: Evaluating Joint Predictions NIPS 2022 Evaluating high-order predictive distributions in deep learning UAI 2022 Deciding What to Learn: A Rate-Distortion Approach ICML 2021 The Value of Information When Deciding What to Learn NIPS 2021 Hypermodels for Exploration ICLR 2020 Behaviour Suite for Reinforcement Learning ICLR 2020 On Efficiency in Hierarchical Reinforcement Learning NIPS 2020 Information-Theoretic Confidence Bounds for Reinforcement Learning NIPS 2019 Deep Exploration via Randomized Value Functions JMLR 2019 On the Performance of Thompson Sampling on Logistic Bandits COLT 2019 Scalable Coordinated Exploration in Concurrent Reinforcement Learning NIPS 2018 An Information-Theoretic Analysis for Thompson Sampling with Many Actions NIPS 2018 Coordinated Exploration in Concurrent Reinforcement Learning ICML 2018 Ensemble Sampling NIPS 2017 Why is Posterior Sampling Better than Optimism for Reinforcement Learning? ICML 2017 Conservative Contextual Linear Bandits NIPS 2017 Deep Exploration via Bootstrapped DQN NIPS 2016 Generalization and Exploration via Randomized Value Functions ICML 2016 An Information-Theoretic Analysis of Thompson Sampling JMLR 2016 Model-based Reinforcement Learning and the Eluder Dimension NIPS 2014 Near-optimal Reinforcement Learning in Factored MDPs NIPS 2014 Learning to Optimize via Information-Directed Sampling NIPS 2014 Eluder Dimension and the Sample Complexity of Optimistic Exploration NIPS 2013 Efficient Exploration and Value Function Generalization in Deterministic Systems NIPS 2013 (More) Efficient Reinforcement Learning via Posterior Sampling NIPS 2013