4 citations · 6 across the 9 of their papers we have counts for
9 papers · 1 filter
Model Agreement via Anchoring
Eric Eaton, Surbhi Goel, Marcel Hussing +4
Numerous lines of aim to control -- the extent to which two machine learning models disagree in their predictions. We adopt a simple and standard noti…
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…
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable
Martin Bertran, Shuai Tang, Michael Kearns +3
Machine unlearning is motivated by desire for data autonomy: a person can request to have their data's influence removed from deployed models, and those models should be updated as…
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…