Daniel Russo

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 42

Top Topics

Temporal Difference

Confidence Bounds

Policy Gradient

Natural Actor Critic

Top Venues

Math. Oper. Res.

Publications

David Cheikhi, Daniel Russo
On the Limited Representational Power of Value Functions and its Links to Statistical (In)Efficiency. CoRR (2024)
Chao Qin, Daniel Russo
Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification. CoRR (2024)
Kelly W. Zhang, Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo
Posterior Sampling via Autoregressive Generation. CoRR (2024)
Seungki Min, Daniel Russo
An Information-Theoretic Analysis of Nonstationary Bandit Learning. ICML (2023)
David Cheikhi, Daniel Russo
On the Statistical Benefits of Temporal Difference Learning. ICML (2023)
Seungki Min, Daniel Russo
An Information-Theoretic Analysis of Nonstationary Bandit Learning. CoRR (2023)
Daniel Russo
Approximation Benefits of Policy Gradient Methods with Aggregated States. Manag. Sci. 69 (11) (2023)
Matias Alvo, Daniel Russo, Yash Kanoria
Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization. CoRR (2023)
Lucas Maystre, Daniel Russo, Yu Zhao
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective. CoRR (2023)
Thomas M. McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. CoRR (2023)
Thomas M. McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. KDD (2023)
Daniel Russo, Benjamin Van Roy
Satisficing in Time-Sensitive Bandit Learning. Math. Oper. Res. 47 (4) (2022)
Chao Qin, Daniel Russo
Adaptivity and Confounding in Multi-Armed Bandit Experiments. CoRR (2022)
Lucas Maystre, Daniel Russo
Temporally-Consistent Survival Analysis. NeurIPS (2022)
Jalaj Bhandari, Daniel Russo
On the Linear Convergence of Policy Gradient Methods for Finite MDPs. AISTATS (2021)
Daniel Russo, Assaf Zeevi, Tianyi Zhang
Learning to Stop with Surprisingly Few Samples. CoRR (2021)
Jalaj Bhandari, Daniel Russo, Raghav Singal
A Finite Time Analysis of Temporal Difference Learning with Linear Function Approximation. Oper. Res. 69 (3) (2021)
Santiago R. Balseiro, Anthony Kim, Daniel Russo
On the Futility of Dynamics in Robust Mechanism Design. Oper. Res. 69 (6) (2021)
Daniel Russo
Technical Note - A Note on the Equivalence of Upper Confidence Bounds and Gittins Indices for Patient Agents. Oper. Res. 69 (1) (2021)
Daniel Russo, Assaf Zeevi, Tianyi Zhang
Learning to Stop with Surprisingly Few Samples. COLT (2021)
Daniel Russo
Simple Bayesian Algorithms for Best-Arm Identification. Oper. Res. 68 (6) (2020)
Daniel Russo
Approximation Benefits of Policy Gradient Methods with Aggregated States. CoRR (2020)
Daniel Russo, James Zou
How Much Does Your Data Exploration Overfit? Controlling Bias via Information Usage. IEEE Trans. Inf. Theory 66 (1) (2020)
Jalaj Bhandari, Daniel Russo
A Note on the Linear Convergence of Policy Gradient Methods. CoRR (2020)
Jalaj Bhandari, Daniel Russo
Global Optimality Guarantees For Policy Gradient Methods. CoRR (2019)
Daniel Russo
A Note on the Equivalence of Upper Confidence Bounds and Gittins Indices for Patient Agents. CoRR (2019)
Daniel Russo
Worst-Case Regret Bounds for Exploration via Randomized Value Functions. CoRR (2019)
Daniel Russo
Worst-Case Regret Bounds for Exploration via Randomized Value Functions. NeurIPS (2019)
Daniel Russo, Benjamin Van Roy
Learning to Optimize via Information-Directed Sampling. Oper. Res. 66 (1) (2018)
Daniel Russo, Benjamin Van Roy
Satisficing in Time-Sensitive Bandit Learning. CoRR (2018)
Jalaj Bhandari, Daniel Russo, Raghav Singal
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation. CoRR (2018)
Jalaj Bhandari, Daniel Russo, Raghav Singal
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation. COLT (2018)
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen
A Tutorial on Thompson Sampling. Found. Trends Mach. Learn. 11 (1) (2018)
Ian Osband, Daniel Russo, Zheng Wen, Benjamin Van Roy
Deep Exploration via Randomized Value Functions. CoRR (2017)
Chao Qin, Diego Klabjan, Daniel Russo
Improving the Expected Improvement Algorithm. CoRR (2017)
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband
A Tutorial on Thompson Sampling. CoRR (2017)
Daniel Russo, David Tse, Benjamin Van Roy
Time-Sensitive Bandit Learning and Satisficing Thompson Sampling. CoRR (2017)
Chao Qin, Diego Klabjan, Daniel Russo
Improving the Expected Improvement Algorithm. NIPS (2017)
Daniel Russo, James Zou
Controlling Bias in Adaptive Data Analysis Using Information Theory. AISTATS (2016)
Daniel Russo
Simple Bayesian Algorithms for Best Arm Identification. COLT (2016)
Daniel Russo, Benjamin Van Roy
An Information-Theoretic Analysis of Thompson Sampling. J. Mach. Learn. Res. 17 (2016)
Daniel Russo
Simple Bayesian Algorithms for Best Arm Identification. CoRR (2016)
Daniel Russo, James Zou
Controlling Bias in Adaptive Data Analysis Using Information Theory. CoRR (2015)
Daniel Russo, Benjamin Van Roy
Learning to Optimize Via Information Directed Sampling. CoRR (2014)
Daniel Russo, Benjamin Van Roy
Learning to Optimize via Information-Directed Sampling. NIPS (2014)
Daniel Russo, Benjamin Van Roy
An Information-Theoretic Analysis of Thompson Sampling. CoRR (2014)
Daniel Russo, Benjamin Van Roy
Learning to Optimize via Posterior Sampling. Math. Oper. Res. 39 (4) (2014)
Daniel Russo, Benjamin Van Roy
Eluder Dimension and the Sample Complexity of Optimistic Exploration. NIPS (2013)
Ian Osband, Daniel Russo, Benjamin Van Roy
(More) Efficient Reinforcement Learning via Posterior Sampling. CoRR (2013)
Ian Osband, Daniel Russo, Benjamin Van Roy
(More) Efficient Reinforcement Learning via Posterior Sampling. NIPS (2013)
Daniel Russo, Benjamin Van Roy
Learning to Optimize Via Posterior Sampling CoRR (2013)
Nick Arnosti, Daniel Russo
Welfare-Improving Cascades and the Effect of Noisy Reviews. WINE (2013)