26 citations · 94 across the 13 of their papers we have counts for
13 papers
Evaluating for the long term: Learnings from industry
Leif Sigerson, Tom Cunningham, Winston Chou +22
Online platforms prioritize long-term business outcomes, yet typical experiments are far too short to measure these outcomes directly. Our goal in this paper is to collect and shar…
Calibrated Recommendations with Contextual Bandits
Diego Feijer, Himan Abdollahpouri, Sanket Gupta +7
Spotify's Home page features a variety of content types, including music, podcasts, and audiobooks. However, historical data is heavily skewed toward music, making it challenging t…
Concurrent Learning with Aggregated States via Randomized Least Squares Value Iteration
Yan Chen, Qinxun Bai, Yiteng Zhang +4
Designing learning agents that explore efficiently in a complex environment has been widely recognized as a fundamental challenge in reinforcement learning. While a number of works…
Selectively Contextual Bandits
Claudia Roberts, Maria Dimakopoulou, Qifeng Qiao +2
Contextual bandits are widely used in industrial personalization systems. These online learning frameworks learn a treatment assignment policy in the presence of treatment effects…
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning
Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou +2
Empirical risk minimization (ERM) is the workhorse of machine learning, whether for classification and regression or for off-policy policy learning, but its model-agnostic guarante…
Post-Contextual-Bandit Inference
Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou +2
Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-commerce, healthcare, and policymaking because they can both improve outcomes for study particip…
Online Multi-Armed Bandits with Adaptive Inference
Maria Dimakopoulou, Zhimei Ren, Zhengyuan Zhou
During online decision making in Multi-Armed Bandits (MAB), one needs to conduct inference on the true mean reward of each arm based on data collected so far at each step. However,…
Sequential causal inference in a single world of connected units
Aurelien Bibaut, Maya Petersen, Nikos Vlassis +2
We consider adaptive designs for a trial involving N individuals that we follow along T time steps. We allow for the variables of one individual to depend on its past and on the pa…
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy +1
We propose a new framework for designing estimators for off-policy evaluation in contextual bandits. Our approach is based on the asymptotically optimal doubly robust estimator, bu…
Balanced Linear Contextual Bandits
Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey +1
Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity…
Scalable Coordinated Exploration in Concurrent Reinforcement Learning
Maria Dimakopoulou, Ian Osband, Benjamin Van Roy
We consider a team of reinforcement learning agents that concurrently operate in a common environment, and we develop an approach to efficient coordinated exploration that is suita…
Coordinated Exploration in Concurrent Reinforcement Learning
Maria Dimakopoulou, Benjamin Van Roy
We consider a team of reinforcement learning agents that concurrently learn to operate in a common environment. We identify three properties - adaptivity, commitment, and diversity…
Estimation Considerations in Contextual Bandits
Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey +1
Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity…