758 citations · 2.2k across the 33 of their papers we have counts for
36 papers
Use and Effects of LLMs in Peer Review: A Randomized Experiment and Survey at ICML 2026
Sunnie S. Y. Kim, Wesley Hanwen Deng, Jennifer Wortman Vaughan +8
LLMs are rapidly reshaping peer review, making it important to understand how reviewers use them in practice and how different LLM-use policies affect review outcomes. We investiga…
Trade-offs in Ensembling, Merging and Routing Among Parameter-Efficient Experts
Sanae Lotfi, Lucas Caccia, Alessandro Sordoni +2
While large language models (LLMs) fine-tuned with lightweight adapters achieve strong performance across diverse tasks, their performance on individual tasks depends on the fine-t…
On the Hardness of Bandit Learning
Nataly Brukhim, Aldo Pacchiano, Miroslav Dudik +1
We study the task of bandit learning, also known as best-arm identification, under the assumption that the true reward function f belongs to a known, but arbitrary, function class…
A structured regression approach for evaluating model performance across intersectional subgroups
Christine Herlihy, Kimberly Truong, Alexandra Chouldechova +1
Disaggregated evaluation is a central task in AI fairness assessment, where the goal is to measure an AI system's performance across different subgroups defined by combinations of…
A Unified Model and Dimension for Interactive Estimation
Nataly Brukhim, Miroslav Dudik, Aldo Pacchiano +1
We study an abstract framework for interactive learning called interactive estimation in which the goal is to estimate a target from its "similarity'' to points queried by the lear…
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning
Alberto Bietti, Chen-Yu Wei, Miroslav Dudík +2
Large-scale machine learning systems often involve data distributed across a collection of users. Federated learning algorithms leverage this structure by communicating model updat…