Daniel Russo - RankMe

59 papers A* 8A 3Journal 42Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Success Conditioning as Policy Improvement: The Optimization Problem Solved by Imitating Success. CoRR Daniel Russo
2025	J	`jnl`	Contextual Thompson Sampling via Generation of Missing Data. CoRR Kelly W. Zhang, Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo
2025	J	`jnl`	Impatient Bandits: Optimizing for the Long-Term Without Delay. CoRR Kelly W. Zhang, Thomas Baldwin-McDonald, Kamil Ciosek, Lucas Maystre, Daniel Russo
2024	J	`jnl`	Global Optimality Guarantees for Policy Gradient Methods. Oper. Res. Jalaj Bhandari, Daniel Russo
2024	J	`jnl`	On the Limited Representational Power of Value Functions and its Links to Statistical (In)Efficiency. CoRR David Cheikhi, Daniel Russo
2024	J	`jnl`	Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification. CoRR Chao Qin, Daniel Russo
2024	J	`jnl`	Posterior Sampling via Autoregressive Generation. CoRR Kelly W. Zhang, Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo
2024	—	`ed.`	Proceedings of the Workshop on Strategic and Utility-aware REcommendations (SURE 2024) co-located with 18th ACM Conference on Recommender Systems (RecSys 2024), Bari, Italy, 14th-18th October 2024. SURE@RecSys Himan Abdollahpouri, Tonia Danylenko, Masoud Mansoury, Babak Loni, Daniel Russo, Mihajlo Grbovic
2024	A	`conf`	SURE 2024: Workshop on Strategic and Utility-aware REcommendation. RecSys Himan Abdollahpouri, Tonia Danylenko, Masoud Mansoury, Babak Loni, Daniel Russo, Mihajlo Grbovic
2023	A*	`conf`	An Information-Theoretic Analysis of Nonstationary Bandit Learning. ICML Seungki Min, Daniel Russo
2023	J	`jnl`	An Information-Theoretic Analysis of Nonstationary Bandit Learning. CoRR Seungki Min, Daniel Russo
2023	J	`jnl`	Approximation Benefits of Policy Gradient Methods with Aggregated States. Manag. Sci. Daniel Russo
2023	A*	`conf`	Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. KDD Thomas M. McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek
2023	J	`jnl`	Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. CoRR Thomas M. McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek
2023	J	`jnl`	Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization. CoRR Matias Alvo, Daniel Russo, Yash Kanoria
2023	A*	`conf`	On the Statistical Benefits of Temporal Difference Learning. ICML David Cheikhi, Daniel Russo
2023	J	`jnl`	On the Statistical Benefits of Temporal Difference Learning. CoRR David Cheikhi, Daniel Russo
2023	J	`jnl`	Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective. CoRR Lucas Maystre, Daniel Russo, Yu Zhao
2022	J	`jnl`	Adaptivity and Confounding in Multi-Armed Bandit Experiments. CoRR Chao Qin, Daniel Russo
2022	J	`jnl`	Satisficing in Time-Sensitive Bandit Learning. Math. Oper. Res. Daniel Russo, Benjamin Van Roy
2022	A*	`conf`	Temporally-Consistent Survival Analysis. NeurIPS Lucas Maystre, Daniel Russo
2021	J	`jnl`	A Finite Time Analysis of Temporal Difference Learning with Linear Function Approximation. Oper. Res. Jalaj Bhandari, Daniel Russo, Raghav Singal
2021	A*	`conf`	Learning to Stop with Surprisingly Few Samples. COLT Daniel Russo, Assaf Zeevi, Tianyi Zhang
2021	J	`jnl`	Learning to Stop with Surprisingly Few Samples. CoRR Daniel Russo, Assaf Zeevi, Tianyi Zhang
2021	J	`jnl`	On the Futility of Dynamics in Robust Mechanism Design. Oper. Res. Santiago R. Balseiro, Anthony Kim, Daniel Russo
2021	A	`conf`	On the Linear Convergence of Policy Gradient Methods for Finite MDPs. AISTATS Jalaj Bhandari, Daniel Russo
2021	J	`jnl`	Technical Note - A Note on the Equivalence of Upper Confidence Bounds and Gittins Indices for Patient Agents. Oper. Res. Daniel Russo
2020	J	`jnl`	A Note on the Linear Convergence of Policy Gradient Methods. CoRR Jalaj Bhandari, Daniel Russo
2020	J	`jnl`	Approximation Benefits of Policy Gradient Methods with Aggregated States. CoRR Daniel Russo
2020	J	`jnl`	How Much Does Your Data Exploration Overfit? Controlling Bias via Information Usage. IEEE Trans. Inf. Theory Daniel Russo, James Zou
2020	J	`jnl`	Simple Bayesian Algorithms for Best-Arm Identification. Oper. Res. Daniel Russo
2019	J	`jnl`	A Note on the Equivalence of Upper Confidence Bounds and Gittins Indices for Patient Agents. CoRR Daniel Russo
2019	J	`jnl`	Global Optimality Guarantees For Policy Gradient Methods. CoRR Jalaj Bhandari, Daniel Russo
2019	A*	`conf`	Worst-Case Regret Bounds for Exploration via Randomized Value Functions. NeurIPS Daniel Russo
2019	J	`jnl`	Worst-Case Regret Bounds for Exploration via Randomized Value Functions. CoRR Daniel Russo
2018	A*	`conf`	A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation. COLT Jalaj Bhandari, Daniel Russo, Raghav Singal
2018	J	`jnl`	A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation. CoRR Jalaj Bhandari, Daniel Russo, Raghav Singal
2018	J	`jnl`	A Tutorial on Thompson Sampling. Found. Trends Mach. Learn. Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen
2018	J	`jnl`	Learning to Optimize via Information-Directed Sampling. Oper. Res. Daniel Russo, Benjamin Van Roy
2018	J	`jnl`	Satisficing in Time-Sensitive Bandit Learning. CoRR Daniel Russo, Benjamin Van Roy
2017	J	`jnl`	A Tutorial on Thompson Sampling. CoRR Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband
2017	J	`jnl`	Deep Exploration via Randomized Value Functions. CoRR Ian Osband, Daniel Russo, Zheng Wen, Benjamin Van Roy
2017	—	`conf`	Improving the Expected Improvement Algorithm. NIPS Chao Qin, Diego Klabjan, Daniel Russo
2017	J	`jnl`	Improving the Expected Improvement Algorithm. CoRR Chao Qin, Diego Klabjan, Daniel Russo
2017	J	`jnl`	Time-Sensitive Bandit Learning and Satisficing Thompson Sampling. CoRR Daniel Russo, David Tse, Benjamin Van Roy
2016	J	`jnl`	An Information-Theoretic Analysis of Thompson Sampling. J. Mach. Learn. Res. Daniel Russo, Benjamin Van Roy
2016	A	`conf`	Controlling Bias in Adaptive Data Analysis Using Information Theory. AISTATS Daniel Russo, James Zou
2016	A*	`conf`	Simple Bayesian Algorithms for Best Arm Identification. COLT Daniel Russo
2016	J	`jnl`	Simple Bayesian Algorithms for Best Arm Identification. CoRR Daniel Russo
2015	J	`jnl`	Controlling Bias in Adaptive Data Analysis Using Information Theory. CoRR Daniel Russo, James Zou
2014	J	`jnl`	An Information-Theoretic Analysis of Thompson Sampling. CoRR Daniel Russo, Benjamin Van Roy
2014	J	`jnl`	Learning to Optimize Via Information Directed Sampling. CoRR Daniel Russo, Benjamin Van Roy
2014	—	`conf`	Learning to Optimize via Information-Directed Sampling. NIPS Daniel Russo, Benjamin Van Roy
2014	J	`jnl`	Learning to Optimize via Posterior Sampling. Math. Oper. Res. Daniel Russo, Benjamin Van Roy
2013	—	`conf`	(More) Efficient Reinforcement Learning via Posterior Sampling. NIPS Ian Osband, Daniel Russo, Benjamin Van Roy
2013	J	`jnl`	(More) Efficient Reinforcement Learning via Posterior Sampling. CoRR Ian Osband, Daniel Russo, Benjamin Van Roy
2013	—	`conf`	Eluder Dimension and the Sample Complexity of Optimistic Exploration. NIPS Daniel Russo, Benjamin Van Roy
2013	J	`jnl`	Learning to Optimize Via Posterior Sampling CoRR Daniel Russo, Benjamin Van Roy
2013	—	`conf`	Welfare-Improving Cascades and the Effect of Noisy Reviews. WINE Nick Arnosti, Daniel Russo