5 papers
Adaptive Policy Learning Under Unknown Network Interference
Aidan Gleich, Eric Laber, Alexander Volfovsky
Adaptive experimentation under unknown network interference requires solving two coupled problems: (i) learning the underlying dynamics of interference among units and (ii) using t…
Optimal Data Integration and Adaptive Sampling for Efficient Treatment Effect Estimation
Yen-Chun Liu, Alexander Volfovsky, German Schnaidt +2
This study addresses the challenge of estimating average treatment effects (ATEs) for advertising campaigns in online marketplaces where complete randomized experimentation is infe…
Scalable Policy Maximization Under Network Interference
Aidan Gleich, Eric Laber, Alexander Volfovsky
Many interventions, such as vaccines in clinical trials or coupons in online marketplaces, must be assigned sequentially without full knowledge of their effects. Multi-armed bandit…
Reinforcement Learning for Respondent-Driven Sampling
Justin Weltz, Angela Yoon, Yichi Zhang +2
Respondent-driven sampling (RDS) is widely used to study hidden or hard-to-reach populations by incentivizing study participants to recruit their social connections. The success an…
Experimental Designs for Heteroskedastic Variance
Justin Weltz, Tanner Fiez, Alexander Volfovsky +4
Most linear experimental design problems assume homogeneous variance although heteroskedastic noise is present in many realistic settings. Let a learner have access to a finite set…