most citedLarge Deviation Methods for Approximate Probabilistic Inference

51 citations · 153 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG201313 cited

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…

cs.LG201351 cited

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…

cs.AI20137 cited

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…

cs.GT201336 cited

Fast Planning in Stochastic Games

Michael Kearns, Yishay Mansour, Satinder Singh

Stochastic games generalize Markov decision processes (MDPs) to a multiagent setting by allowing the state transitions to depend jointly on all player actions, and having rewards d…

cs.GT201246 cited

Efficient Nash Computation in Large Population Games with Bounded Influence

Michael Kearns, Yishay Mansour

We introduce a general representation of large-population games in which each player s influence ON the others IS centralized AND limited, but may otherwise be arbitrary.This repre…