41 citations · 86 across the 15 of their papers we have counts for
4 papers · 1 filter
Learning to target with network interference
Xiaomeng Wang, Hamsa Bastani, Osbert Bastani +1
This paper studies adaptive targeting under network interference in a bandit setting, where treatments applied to one individual may affect others through spillover effects. We con…
Policy Learning with Adaptively Collected Data
Ruohan Zhan, Zhimei Ren, Susan Athey +1
Learning optimal policies from historical data enables personalization in a wide variety of applications including healthcare, digital recommendations, and online education. The gr…
Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits
Zhimei Ren, Zhengyuan Zhou
We study the problem of dynamic batch learning in high-dimensional sparse linear contextual bandits, where a decision maker, under a given maximum-number-of-batch constraint and on…
Batched Multi-armed Bandits Problem
Zijun Gao, Yanjun Han, Zhimei Ren +1
In this paper, we study the multi-armed bandit problem in the batched setting where the employed policy must split data into a small number of batches. While the minimax regret for…