51 citations · 160 across the 8 of their papers we have counts for
8 papers
Replicable Reinforcement Learning
Eric Eaton, Marcel Hussing, Michael Kearns +1
The replicability crisis in the social, behavioral, and data sciences has led to the formulation of algorithm frameworks for replicability -- i.e., a requirement that an algorithm…
AI Model Disgorgement: Methods and Choices
Alessandro Achille, Michael Kearns, Carson Klingenberg +1
Responsible use of data is an indispensable part of any machine learning (ML) implementation. ML developers must carefully collect and curate their datasets, and document their pro…
Private Synthetic Data for Multitask Learning and Marginal Queries
Giuseppe Vietri, Cedric Archambeau, Sergul Aydore +6
We provide a differentially private algorithm for producing synthetic data simultaneously useful for multiple tasks: marginal queries and multitask machine learning (ML). A key inn…
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering
Michael Kearns, Yishay Mansour, Andrew Y. Ng
Assignment methods are at the heart of many algorithms for unsupervised learning and clustering - in particular, the well-known K-means and Expectation-Maximization (EM) algorithms…
Large Deviation Methods for Approximate Probabilistic Inference
Michael Kearns, Lawrence Saul
We study two-layer belief networks of binary random variables in which the conditional probabilities Pr[childlparents] depend monotonically on weighted sums of the parents. In larg…
Exact Inference of Hidden Structure from Sample Data in Noisy-OR Networks
Michael Kearns, Yishay Mansour
In the literature on graphical models, there has been increased attention paid to the problems of learning hidden structure (see Heckerman [H96] for survey) and causal mechanisms f…