activity
20192025
most citedDistributionally Robust Optimal Auction Design under Mean Constraints

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2025

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…

cs.LG2024

Differentiable Discrete Event Simulation for Queuing Network Control

Ethan Che, Jing Dong, Hongseok Namkoong

Queuing network control is essential for managing congestion in job-processing systems such as service systems, communication networks, and manufacturing processes. Despite growing…

cs.LG2024

AExGym: Benchmarks and Environments for Adaptive Experimentation

Jimmy Wang, Ethan Che, Daniel R. Jiang +1

Innovations across science and industry are evaluated using randomized trials (a.k.a. A/B tests). While simple and robust, such static designs are inefficient or infeasible for tes…

cs.LG2024

Optimization-Driven Adaptive Experimentation

Ethan Che, Daniel R. Jiang, Hongseok Namkoong +1

Real-world experiments involve batched & delayed feedback, non-stationarity, multiple objectives & constraints, and (often some) personalization. Tailoring adaptive methods to addr…

cs.LG2023

Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches

Ethan Che, Hongseok Namkoong

Standard bandit algorithms that assume continual reallocation of measurement effort are challenging to implement due to delayed feedback and infrastructural/organizational difficul…

econ.TH2019★ 1 cited

Distributionally Robust Optimal Auction Design under Mean Constraints

Ethan Che

We study a seller who sells a single good to multiple bidders with uncertainty over the joint distribution of bidders' valuations, as well as bidders' higher-order beliefs about th…