(More) Efficient Reinforcement Learning via Posterior Sampling. - Ian Osband; Daniel Russo 001; Benjamin Van Roy (NIPS, 2013)

Eluder Dimension and the Sample Complexity of Optimistic Exploration. - Daniel Russo 001; Benjamin Van Roy (NIPS, 2013)

Welfare-Improving Cascades and the Effect of Noisy Reviews. - Nick Arnosti; Daniel Russo 001 (WINE, 2013)

Learning to Optimize via Posterior Sampling. - Daniel Russo 001; Benjamin Van Roy (Math. Oper. Res., 2014)

Controlling Bias in Adaptive Data Analysis Using Information Theory. - Daniel Russo 001; James Zou (AISTATS, 2016)

An Information-Theoretic Analysis of Thompson Sampling. - Daniel Russo 001; Benjamin Van Roy (J. Mach. Learn. Res., 2016)

Improving the Expected Improvement Algorithm. - Chao Qin; Diego Klabjan; Daniel Russo 001 (NIPS, 2017)

A Tutorial on Thompson Sampling. - Daniel Russo 001; Benjamin Van Roy; Abbas Kazerouni; Ian Osband (CoRR, 2017)

Time-Sensitive Bandit Learning and Satisficing Thompson Sampling. - Daniel Russo 001; David Tse; Benjamin Van Roy (CoRR, 2017)

A Tutorial on Thompson Sampling. - Daniel Russo 001; Benjamin Van Roy; Abbas Kazerouni; Ian Osband; Zheng Wen 002 (Found. Trends Mach. Learn., 2018)

Learning to Optimize via Information-Directed Sampling. - Daniel Russo 001; Benjamin Van Roy (Oper. Res., 2018)

Worst-Case Regret Bounds for Exploration via Randomized Value Functions. - Daniel Russo 001 (NeurIPS, 2019)

A Note on the Equivalence of Upper Confidence Bounds and Gittins Indices for Patient Agents. - Daniel Russo 001 (CoRR, 2019)

Deep Exploration via Randomized Value Functions. - Ian Osband; Benjamin Van Roy; Daniel J. Russo; Zheng Wen 002 (J. Mach. Learn. Res., 2019)

Policy Gradient Optimization of Thompson Sampling Policies. - Seungki Min; Ciamac C. Moallemi; Daniel J. Russo (CoRR, 2020)

A Note on the Linear Convergence of Policy Gradient Methods. - Jalaj Bhandari; Daniel Russo 001 (CoRR, 2020)

Simple Bayesian Algorithms for Best-Arm Identification. - Daniel Russo 001 (Oper. Res., 2020)

How Much Does Your Data Exploration Overfit? Controlling Bias via Information Usage. - Daniel Russo 001; James Zou 001 (IEEE Trans. Inf. Theory, 2020)

On the Linear Convergence of Policy Gradient Methods for Finite MDPs. - Jalaj Bhandari; Daniel Russo 001 (AISTATS, 2021)

Learning to Stop with Surprisingly Few Samples. - Daniel Russo 001; Assaf Zeevi; Tianyi Zhang (COLT, 2021)

On the Futility of Dynamics in Robust Mechanism Design. - Santiago R. Balseiro; Anthony Kim; Daniel Russo 001 (Oper. Res., 2021)

A Finite Time Analysis of Temporal Difference Learning with Linear Function Approximation. - Jalaj Bhandari; Daniel Russo 001; Raghav Singal (Oper. Res., 2021)

Technical Note - A Note on the Equivalence of Upper Confidence Bounds and Gittins Indices for Patient Agents. - Daniel Russo 001 (Oper. Res., 2021)

Temporally-Consistent Survival Analysis. - Lucas Maystre; Daniel Russo 001 (NeurIPS, 2022)

Adaptivity and Confounding in Multi-Armed Bandit Experiments. - Chao Qin; Daniel Russo 001 (CoRR, 2022)

Satisficing in Time-Sensitive Bandit Learning. - Daniel Russo 001; Benjamin Van Roy (Math. Oper. Res., 2022)

On the Statistical Benefits of Temporal Difference Learning. - David Cheikhi; Daniel Russo 001 (ICML, 2023)

An Information-Theoretic Analysis of Nonstationary Bandit Learning. - Seungki Min; Daniel Russo 001 (ICML, 2023)

Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. - Thomas M. McDonald 001; Lucas Maystre; Mounia Lalmas; Daniel Russo 001; Kamil Ciosek (KDD, 2023)

Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective. - Lucas Maystre; Daniel Russo 001; Yu Zhao (CoRR, 2023)

Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization. - Matias Alvo; Daniel Russo 001; Yash Kanoria (CoRR, 2023)

Approximation Benefits of Policy Gradient Methods with Aggregated States. - Daniel Russo 001 (Manag. Sci., 2023)

SURE 2024: Workshop on Strategic and Utility-aware REcommendation. - Himan Abdollahpouri; Tonia Danylenko; Masoud Mansoury; Babak Loni; Daniel Russo 001; Mihajlo Grbovic (RecSys, 2024)

Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification. - Chao Qin; Daniel Russo 001 (CoRR, 2024)

On the Limited Representational Power of Value Functions and its Links to Statistical (In)Efficiency. - David Cheikhi; Daniel Russo 001 (CoRR, 2024)

Posterior Sampling via Autoregressive Generation. - Kelly W. Zhang; Tiffany Tianhui Cai; Hongseok Namkoong; Daniel Russo 001 (CoRR, 2024)

Global Optimality Guarantees for Policy Gradient Methods. - Jalaj Bhandari; Daniel Russo 001 (Oper. Res., 2024)

Impatient Bandits: Optimizing for the Long-Term Without Delay. - Kelly W. Zhang; Thomas Baldwin-McDonald; Kamil Ciosek; Lucas Maystre; Daniel Russo 001 (CoRR, 2025)

Contextual Thompson Sampling via Generation of Missing Data. - Kelly W. Zhang; Tiffany Tianhui Cai; Hongseok Namkoong; Daniel Russo 001 (CoRR, 2025)