research
∙
07/05/2023
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
Simple regret minimization is a critical problem in learning optimal tre...
share
research
∙
02/01/2023
Selective Uncertainty Propagation in Offline RL
We study the finite-horizon offline reinforcement learning (RL) problem....
research
∙
11/22/2022
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning
We design and implement an adaptive experiment (a “contextual bandit”) t...
research
∙
03/30/2022
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracle
Many popular contextual bandit algorithms estimate reward models to info...
research
∙
06/11/2021
Optimal Model Selection in Contextual Bandits with Many Classes via Offline Oracles
We study the problem of model selection for contextual bandits, in which...
research
∙
02/26/2021
Adapting to misspecification in contextual bandits with offline regression oracles
Computationally efficient contextual bandits are often based on estimati...
research
∙
10/25/2020
Tractable contextual bandits beyond realizability
Tractable contextual bandit algorithms often rely on the realizability a...
research
∙
02/23/2020
Survey Bandits with Regret Guarantees
We consider a variant of the contextual bandit problem. In standard cont...
research
∙
11/21/2018
On the Proximity of Markets with Integral Equilibria
We study Fisher markets that admit equilibria wherein each good is integ...
research
∙
01/27/2018
Greedy Algorithms for Maximizing Nash Social Welfare
We study the problem of fairly allocating a set of indivisible goods amo...
research
∙
11/21/2017
