Daniel Russo

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 27 papers between 2013 and 2022.

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Timeline

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Bibliography

2022
Adaptivity and Confounding in Multi-Armed Bandit Experiments.
CoRR, 2022

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

A Finite Time Analysis of Temporal Difference Learning with Linear Function Approximation.
Oper. Res., 2021

On the Futility of Dynamics in Robust Mechanism Design.
Oper. Res., 2021

Learning to Stop with Surprisingly Few Samples.
Proceedings of the Conference on Learning Theory, 2021

On the Linear Convergence of Policy Gradient Methods for Finite MDPs.
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.
IEEE Trans. Inf. Theory, 2020

Simple Bayesian Algorithms for Best-Arm Identification.
Oper. Res., 2020

Approximation Benefits of Policy Gradient Methods with Aggregated States.
CoRR, 2020

A Note on the Linear Convergence of Policy Gradient Methods.
CoRR, 2020

Policy Gradient Optimization of Thompson Sampling Policies.
CoRR, 2020

2019
Deep Exploration via Randomized Value Functions.
J. Mach. Learn. Res., 2019

Global Optimality Guarantees For Policy Gradient Methods.
CoRR, 2019

A Note on the Equivalence of Upper Confidence Bounds and Gittins Indices for Patient Agents.
CoRR, 2019

Worst-Case Regret Bounds for Exploration via Randomized Value Functions.
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.
Oper. Res., 2018

A Tutorial on Thompson Sampling.
Found. Trends Mach. Learn., 2018

Satisficing in Time-Sensitive Bandit Learning.
CoRR, 2018

2017
Time-Sensitive Bandit Learning and Satisficing Thompson Sampling.
CoRR, 2017

A Tutorial on Thompson Sampling.
CoRR, 2017

Improving the Expected Improvement Algorithm.
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.
J. Mach. Learn. Res., 2016

Controlling Bias in Adaptive Data Analysis Using Information Theory.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2014
Learning to Optimize via Posterior Sampling.
Math. Oper. Res., 2014

2013
Welfare-Improving Cascades and the Effect of Noisy Reviews.
Proceedings of the Web and Internet Economics - 9th International Conference, 2013

Eluder Dimension and the Sample Complexity of Optimistic Exploration.
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.
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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