148 citations · 972 across the 92 of their papers we have counts for
10 papers · 1 filter
Exploration in the Limit
Brian M. Cho, Nathan Kallus
In fixed-confidence best arm identification (BAI), the objective is to quickly identify the optimal option while controlling the probability of error below a desired threshold. Des…
Experimentation Under Non-stationary Interference
Su Jia, Peter Frazier, Nathan Kallus +1
We study the estimation of the ATE in randomized controlled trials under a dynamically evolving interference structure. This setting arises in applications such as ride-sharing, wh…
Entropy After </Think> for reasoning model early exiting
Xi Wang, James McInerney, Lequn Wang +1
Reasoning LLMs show improved performance with longer chains of thought. However, recent work has highlighted their tendency to overthink, continuing to revise answers even after re…
Optimization of Epsilon-Greedy Exploration
Ethan Che, Hakan Ceylan, James McInerney +1
Modern recommendation systems rely on exploration to learn user preferences for new items, typically implementing uniform exploration policies (e.g., epsilon-greedy) due to their s…
Simulation-Based Inference for Adaptive Experiments
Brian M Cho, Aurélien Bibaut, Nathan Kallus
Multi-arm bandit experimental designs are increasingly being adopted over standard randomized trials due to their potential to improve outcomes for study participants, enable faste…
Efficient Adaptive Experimentation with Noncompliance
Miruna Oprescu, Brian M Cho, Nathan Kallus
We study the problem of estimating the average treatment effect (ATE) in adaptive experiments where treatment can only be encouraged -- rather than directly assigned -- via a binar…