Daniel Russo – Research

A Tutorial on Thompson Sampling
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, and Zheng Wen

Deep Exploration via Randomized Value Functions
Ian Osband, Daniel Russo, Zheng Wen, and Benjamin Van Roy

Improving the Expected Improvement Algorithm
Chao Qin, Diego Klabjan and Daniel Russo

Time-Sensitive Bandit Learning and Satisficing Thompson Sampling
Daniel Russo, David Tse, and Benjamin Van Roy

Simple Bayesian Algorithms for Best Arm Identification
Daniel Russo
Journal version in submission
Prelimnary version appeared in COLT 2016
First place, INFORMS JFIG paper competition.

Controling Bias in Adaptive Data Analysis Using Information Theory
Daniel Russo and James Zou
Journal version in submission
Preliminary version accepted at AISTATS 2016 (full oral presentation; top 7% of submissions).

Learning to Optimize Via Information Directed Sampling
Daniel Russo and Benjamin Van Roy
Second round at Operations Research
Prelimnary version appeared at NIPS 2014
First place, INFORMS George Nicholson 2014 student paper competition.

An Information-Theoretic Analysis of Thompson Sampling
Daniel Russo and Benjamin Van Roy
Journal of Machine Learning Research, 2016

Learning to Optimize Via Posterior Sampling
Daniel Russo and Benjamin Van Roy
Mathematics of Operations Research. Vol. 39. No. 4, pp. 1221-1243, 2014.

Eluder Dimension and the Sample Complexity of Optimistic Exploration
Daniel Russo and Benjamin Van Roy
NIPS 2013 (full oral presentation; top 1.4% of submissions).

(More) Efficient Reinforcement Learning via Posterior Sampling
Ian Osband, Daniel Russo, and Benjamin Van Roy
NIPS 2013.

Welfare-Improving Cascades and the Effect of Noisy Reviews
Nick Arnosti and Daniel Russo
WINE 2013.