3 papers
stat.ML2026
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…
cs.LG2025
Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality
Ying Jin, Zhimei Ren, Zhuoran Yang +1
This paper studies offline policy learning, which aims at utilizing observations collected a priori (from either fixed or adaptively evolving behavior policies) to learn an optimal…
cs.LG2025
Distributionally Robust Policy Learning under Concept Drifts
Jingyuan Wang, Zhimei Ren, Ruohan Zhan +1
Distributionally robust policy learning aims to find a policy that performs well under the worst-case distributional shift, and yet most existing methods for robust policy learning…