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Daniel Russo

Orcid: 0000-0001-5926-8624

Affiliations:

Columbia Business School
Microsoft Research
Northwestern's Kellogg School of Management
Stanford University, Department of Management Science and Engineering

According to our database¹, Daniel Russo authored at least 37 papers between 2013 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Links

Online presence:

On csauthors.net:

Bibliography

2024

Global Optimality Guarantees for Policy Gradient Methods.

[BibT_eX]

[DOI]

,

Oper. Res., 2024

Posterior Sampling via Autoregressive Generation.

[BibT_eX]

[DOI]

,

Tiffany Tianhui Cai

,

Hongseok Namkoong

,

CoRR, 2024

On the Limited Representational Power of Value Functions and its Links to Statistical (In)Efficiency.

[BibT_eX]

[DOI]

,

CoRR, 2024

Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification.

[BibT_eX]

[DOI]

,

CoRR, 2024

SURE 2024: Workshop on Strategic and Utility-aware REcommendation.

[BibT_eX]

[DOI]

Himan Abdollahpouri

,

Tonia Danylenko

,

Masoud Mansoury

,

,

,

Mihajlo Grbovic

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

2023

Approximation Benefits of Policy Gradient Methods with Aggregated States.

[BibT_eX]

[DOI]

Manag. Sci., November, 2023

Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization.

[BibT_eX]

[DOI]

,

,

CoRR, 2023

Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective.

[BibT_eX]

[DOI]

,

,

CoRR, 2023

Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay.

[BibT_eX]

[DOI]

Thomas M. McDonald

,

,

,

,

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

An Information-Theoretic Analysis of Nonstationary Bandit Learning.

[BibT_eX]

[DOI]

,

Proceedings of the International Conference on Machine Learning, 2023

On the Statistical Benefits of Temporal Difference Learning.

[BibT_eX]

[DOI]

,

Proceedings of the International Conference on Machine Learning, 2023

2022

Satisficing in Time-Sensitive Bandit Learning.

[BibT_eX]

[DOI]

,

Benjamin Van Roy

Math. Oper. Res., November, 2022

Adaptivity and Confounding in Multi-Armed Bandit Experiments.

[BibT_eX]

[DOI]

,

CoRR, 2022

Temporally-Consistent Survival Analysis.

[BibT_eX]

[DOI]

,

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Technical Note - A Note on the Equivalence of Upper Confidence Bounds and Gittins Indices for Patient Agents.

[BibT_eX]

[DOI]

Oper. Res., 2021

A Finite Time Analysis of Temporal Difference Learning with Linear Function Approximation.

[BibT_eX]

[DOI]

,

,

Oper. Res., 2021

On the Futility of Dynamics in Robust Mechanism Design.

[BibT_eX]

[DOI]

Santiago R. Balseiro

,

,

Oper. Res., 2021

Learning to Stop with Surprisingly Few Samples.

[BibT_eX]

[DOI]

,

,

Proceedings of the Conference on Learning Theory, 2021

On the Linear Convergence of Policy Gradient Methods for Finite MDPs.

[BibT_eX]

[DOI]

,

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

How Much Does Your Data Exploration Overfit? Controlling Bias via Information Usage.

[BibT_eX]

[DOI]

,

IEEE Trans. Inf. Theory, 2020

Simple Bayesian Algorithms for Best-Arm Identification.

[BibT_eX]

[DOI]

Oper. Res., 2020

A Note on the Linear Convergence of Policy Gradient Methods.

[BibT_eX]

[DOI]

,

CoRR, 2020

Policy Gradient Optimization of Thompson Sampling Policies.

[BibT_eX]

[DOI]

,

Ciamac C. Moallemi

,

Daniel J. Russo

CoRR, 2020

2019

Deep Exploration via Randomized Value Functions.

[BibT_eX]

[DOI]

,

Benjamin Van Roy

,

Daniel J. Russo

,

J. Mach. Learn. Res., 2019

A Note on the Equivalence of Upper Confidence Bounds and Gittins Indices for Patient Agents.

[BibT_eX]

[DOI]

CoRR, 2019

Worst-Case Regret Bounds for Exploration via Randomized Value Functions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Learning to Optimize via Information-Directed Sampling.

[BibT_eX]

[DOI]

,

Benjamin Van Roy

Oper. Res., 2018

A Tutorial on Thompson Sampling.

[BibT_eX]

[DOI]

,

Benjamin Van Roy

,

Abbas Kazerouni

,

,

Found. Trends Mach. Learn., 2018

2017

Time-Sensitive Bandit Learning and Satisficing Thompson Sampling.

[BibT_eX]

[DOI]

,

,

Benjamin Van Roy

CoRR, 2017

A Tutorial on Thompson Sampling.

[BibT_eX]

[DOI]

,

Benjamin Van Roy

,

Abbas Kazerouni

,

CoRR, 2017

Improving the Expected Improvement Algorithm.

[BibT_eX]

[DOI]

,

,

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

An Information-Theoretic Analysis of Thompson Sampling.

[BibT_eX]

[DOI]

,

Benjamin Van Roy

J. Mach. Learn. Res., 2016

Controlling Bias in Adaptive Data Analysis Using Information Theory.

[BibT_eX]

[DOI]

,

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2014

Learning to Optimize via Posterior Sampling.

[BibT_eX]

[DOI]

,

Benjamin Van Roy

Math. Oper. Res., 2014

2013

Welfare-Improving Cascades and the Effect of Noisy Reviews.

[BibT_eX]

[DOI]

,

Proceedings of the Web and Internet Economics - 9th International Conference, 2013

Eluder Dimension and the Sample Complexity of Optimistic Exploration.

[BibT_eX]

[DOI]

,

Benjamin Van Roy

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

(More) Efficient Reinforcement Learning via Posterior Sampling.

[BibT_eX]

[DOI]

,

,

Benjamin Van Roy

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

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