2 citations · 2 across the 9 of their papers we have counts for
9 papers
What Fits (Into Few Tokens) Doesn't Overfit: Compression and Generalization in ML Research Agents
Martin Andres Bertran, Aaron Roth, Zhiwei Steven Wu
Reusing a held-out benchmark adaptively should, in principle, invite overfitting. Yet benchmark-driven machine learning (ML) has produced surprisingly little overfitting in practic…
Replicable Reinforcement Learning with Linear Function Approximation
Eric Eaton, Marcel Hussing, Michael Kearns +3
Replication of experimental results has been a challenge faced by many scientific disciplines, including the field of machine learning. Recent work on the theory of machine learnin…
Networked Information Aggregation via Machine Learning
Michael Kearns, Aaron Roth, Emily Ryu
We study a distributed learning problem in which learning agents are embedded in a directed acyclic graph (DAG). There is a fixed and arbitrary distribution over feature/label pair…
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces
Eric Eaton, Marcel Hussing, Michael Kearns +3
In traditional reinforcement learning (RL), the learner aims to solve a single objective optimization problem: find the policy that maximizes expected reward. However, in many real…
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generation
Tobias Leemann, Periklis Petridis, Giuseppe Vietri +3
While retrieval-augmented generation (RAG) has been shown to enhance factuality of large language model (LLM) outputs, LLMs still suffer from hallucination, generating incorrect or…
Model Ensembling for Constrained Optimization
Ira Globus-Harris, Varun Gupta, Michael Kearns +1
There is a long history in machine learning of model ensembling, beginning with boosting and bagging and continuing to the present day. Much of this history has focused on combinin…
Oracle-Efficient Reinforcement Learning for Max Value Ensembles
Marcel Hussing, Michael Kearns, Aaron Roth +2
Reinforcement learning (RL) in large or infinite state spaces is notoriously challenging, both theoretically (where worst-case sample and computational complexities must scale with…
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning
Ira Globus-Harris, Declan Harrison, Michael Kearns +2
Crowdsourced machine learning on competition platforms such as Kaggle is a popular and often effective method for generating accurate models. Typically, teams vie for the most accu…
Membership Inference Attacks on Diffusion Models via Quantile Regression
Shuai Tang, Zhiwei Steven Wu, Sergul Aydore +2
Recently, diffusion models have become popular tools for image synthesis because of their high-quality outputs. However, like other large-scale models, they may leak private inform…