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Daniel Russo 0001
Person information
- affiliation: Columbia Business School
- affiliation: Microsoft Research
- affiliation: Northwestern's Kellogg School of Management
- affiliation: Stanford University, Department of Management Science and Engineering
Other persons with the same name
- Daniel Russo 0002
— Aalborg University, Copenhagen, Denmark (and 2 more)
- Daniel Russo 0003 — University of Waterloo, Waterloo, Canada
- Daniel Russo 0004 — Fondazione Bruno Kessler, Trento, Italy
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2020 – today
- 2024
- [j13]Jalaj Bhandari
Global Optimality Guarantees for Policy Gradient Methods. Oper. Res. 72(5): 1906-1927 (2024) - [c16]Himan Abdollahpouri
SURE 2024: Workshop on Strategic and Utility-aware REcommendation. RecSys 2024: 1210-1212 - [i28]Chao Qin, Daniel Russo:
Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification. CoRR abs/2402.10592 (2024) - [i27]David Cheikhi, Daniel Russo:
On the Limited Representational Power of Value Functions and its Links to Statistical (In)Efficiency. CoRR abs/2403.07136 (2024) - [i26]Kelly W. Zhang, Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo:
Posterior Sampling via Autoregressive Generation. CoRR abs/2405.19466 (2024) - 2023
- [j12]Daniel Russo
Approximation Benefits of Policy Gradient Methods with Aggregated States. Manag. Sci. 69(11): 6898-6911 (2023) - [c15]David Cheikhi, Daniel Russo:
On the Statistical Benefits of Temporal Difference Learning. ICML 2023: 4269-4293 - [c14]Seungki Min, Daniel Russo:
An Information-Theoretic Analysis of Nonstationary Bandit Learning. ICML 2023: 24831-24849 - [c13]Thomas M. McDonald
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. KDD 2023: 1687-1697 - [i25]David Cheikhi, Daniel Russo:
On the Statistical Benefits of Temporal Difference Learning. CoRR abs/2301.13289 (2023) - [i24]Lucas Maystre, Daniel Russo, Yu Zhao:
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective. CoRR abs/2302.03561 (2023) - [i23]Seungki Min, Daniel Russo:
An Information-Theoretic Analysis of Nonstationary Bandit Learning. CoRR abs/2302.04452 (2023) - [i22]Matias Alvo, Daniel Russo, Yash Kanoria:
Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization. CoRR abs/2306.11246 (2023) - [i21]Thomas M. McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek:
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. CoRR abs/2307.09943 (2023) - 2022
- [j11]Daniel Russo
Satisficing in Time-Sensitive Bandit Learning. Math. Oper. Res. 47(4): 2815-2839 (2022) - [c12]Lucas Maystre, Daniel Russo:
Temporally-Consistent Survival Analysis. NeurIPS 2022 - [i20]Chao Qin, Daniel Russo:
Adaptivity and Confounding in Multi-Armed Bandit Experiments. CoRR abs/2202.09036 (2022) - 2021
- [j10]Daniel Russo
Technical Note - A Note on the Equivalence of Upper Confidence Bounds and Gittins Indices for Patient Agents. Oper. Res. 69(1): 273-278 (2021) - [j9]Jalaj Bhandari, Daniel Russo
A Finite Time Analysis of Temporal Difference Learning with Linear Function Approximation. Oper. Res. 69(3): 950-973 (2021) - [j8]Santiago R. Balseiro
On the Futility of Dynamics in Robust Mechanism Design. Oper. Res. 69(6): 1767-1783 (2021) - [c11]Jalaj Bhandari, Daniel Russo:
On the Linear Convergence of Policy Gradient Methods for Finite MDPs. AISTATS 2021: 2386-2394 - [c10]Daniel Russo, Assaf Zeevi, Tianyi Zhang:
Learning to Stop with Surprisingly Few Samples. COLT 2021: 3887-3888 - [i19]Daniel Russo, Assaf Zeevi, Tianyi Zhang:
Learning to Stop with Surprisingly Few Samples. CoRR abs/2102.10025 (2021) - 2020
- [j7]Daniel Russo
Simple Bayesian Algorithms for Best-Arm Identification. Oper. Res. 68(6): 1625-1647 (2020) - [j6]Daniel Russo
How Much Does Your Data Exploration Overfit? Controlling Bias via Information Usage. IEEE Trans. Inf. Theory 66(1): 302-323 (2020) - [i18]Seungki Min, Ciamac C. Moallemi, Daniel J. Russo:
Policy Gradient Optimization of Thompson Sampling Policies. CoRR abs/2006.16507 (2020) - [i17]Jalaj Bhandari, Daniel Russo:
A Note on the Linear Convergence of Policy Gradient Methods. CoRR abs/2007.11120 (2020) - [i16]Daniel Russo:
Approximation Benefits of Policy Gradient Methods with Aggregated States. CoRR abs/2007.11684 (2020)
2010 – 2019
- 2019
- [j5]Ian Osband, Benjamin Van Roy, Daniel J. Russo, Zheng Wen:
Deep Exploration via Randomized Value Functions. J. Mach. Learn. Res. 20: 124:1-124:62 (2019) - [c9]Daniel Russo:
Worst-Case Regret Bounds for Exploration via Randomized Value Functions. NeurIPS 2019: 14410-14420 - [i15]Daniel Russo:
A Note on the Equivalence of Upper Confidence Bounds and Gittins Indices for Patient Agents. CoRR abs/1904.04732 (2019) - [i14]Jalaj Bhandari, Daniel Russo:
Global Optimality Guarantees For Policy Gradient Methods. CoRR abs/1906.01786 (2019) - [i13]Daniel Russo:
Worst-Case Regret Bounds for Exploration via Randomized Value Functions. CoRR abs/1906.02870 (2019) - 2018
- [j4]Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen:
A Tutorial on Thompson Sampling. Found. Trends Mach. Learn. 11(1): 1-96 (2018) - [j3]Daniel Russo
Learning to Optimize via Information-Directed Sampling. Oper. Res. 66(1): 230-252 (2018) - [c8]Jalaj Bhandari, Daniel Russo, Raghav Singal:
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation. COLT 2018: 1691-1692 - [i12]Daniel Russo, Benjamin Van Roy:
Satisficing in Time-Sensitive Bandit Learning. CoRR abs/1803.02855 (2018) - [i11]Jalaj Bhandari, Daniel Russo, Raghav Singal:
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation. CoRR abs/1806.02450 (2018) - 2017
- [c7]Chao Qin, Diego Klabjan, Daniel Russo:
Improving the Expected Improvement Algorithm. NIPS 2017: 5381-5391 - [i10]Ian Osband, Daniel Russo, Zheng Wen, Benjamin Van Roy:
Deep Exploration via Randomized Value Functions. CoRR abs/1703.07608 (2017) - [i9]Daniel Russo, David Tse, Benjamin Van Roy:
Time-Sensitive Bandit Learning and Satisficing Thompson Sampling. CoRR abs/1704.09028 (2017) - [i8]Chao Qin, Diego Klabjan, Daniel Russo:
Improving the Expected Improvement Algorithm. CoRR abs/1705.10033 (2017) - [i7]Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband:
A Tutorial on Thompson Sampling. CoRR abs/1707.02038 (2017) - 2016
- [j2]Daniel Russo, Benjamin Van Roy:
An Information-Theoretic Analysis of Thompson Sampling. J. Mach. Learn. Res. 17: 68:1-68:30 (2016) - [c6]Daniel Russo, James Zou:
Controlling Bias in Adaptive Data Analysis Using Information Theory. AISTATS 2016: 1232-1240 - [c5]Daniel Russo:
Simple Bayesian Algorithms for Best Arm Identification. COLT 2016: 1417-1418 - [i6]Daniel Russo:
Simple Bayesian Algorithms for Best Arm Identification. CoRR abs/1602.08448 (2016) - 2015
- [i5]Daniel Russo, James Zou:
Controlling Bias in Adaptive Data Analysis Using Information Theory. CoRR abs/1511.05219 (2015) - 2014
- [j1]Daniel Russo, Benjamin Van Roy:
Learning to Optimize via Posterior Sampling. Math. Oper. Res. 39(4): 1221-1243 (2014) - [c4]Daniel Russo, Benjamin Van Roy:
Learning to Optimize via Information-Directed Sampling. NIPS 2014: 1583-1591 - [i4]Daniel Russo, Benjamin Van Roy:
An Information-Theoretic Analysis of Thompson Sampling. CoRR abs/1403.5341 (2014) - [i3]Daniel Russo, Benjamin Van Roy:
Learning to Optimize Via Information Directed Sampling. CoRR abs/1403.5556 (2014) - 2013
- [c3]Daniel Russo, Benjamin Van Roy:
Eluder Dimension and the Sample Complexity of Optimistic Exploration. NIPS 2013: 2256-2264 - [c2]Ian Osband, Daniel Russo, Benjamin Van Roy:
(More) Efficient Reinforcement Learning via Posterior Sampling. NIPS 2013: 3003-3011 - [c1]Nick Arnosti, Daniel Russo:
Welfare-Improving Cascades and the Effect of Noisy Reviews. WINE 2013: 405-420 - [i2]Daniel Russo, Benjamin Van Roy:
Learning to Optimize Via Posterior Sampling. CoRR abs/1301.2609 (2013) - [i1]Ian Osband, Daniel Russo, Benjamin Van Roy:
(More) Efficient Reinforcement Learning via Posterior Sampling. CoRR abs/1306.0940 (2013)
Coauthor Index
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last updated on 2024-10-23 21:21 CEST by the dblp team
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