We consider the problem of modeling network interactions and identifying latent groups of network nodes. This problem is challenging due to the facts i) that the network nodes are...
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
In this paper, we propose a variant of the conservativeupdate Count-Min sketch to further reduce the overestimation error incurred. Inspired by ideas from lossy counting, we divid...
We present a dual decomposition approach to the treereweighted belief propagation objective. Each tree in the tree-reweighted bound yields one subproblem, which can be solved with...
Recently the general Game Description Language (GDL) has been extended so as to cover arbitrary games with incomplete/imperfect information. Learning—without human intervention...
In this paper we examine the general problem of generating preferred explanations for observed behavior with respect to a model of the behavior of a dynamical system. This problem...
Shirin Sohrabi, Jorge A. Baier, Sheila A. McIlrait...
We propose a fast batch learning method for linearchain Conditional Random Fields (CRFs) based on Newton-CG methods. Newton-CG methods are a variant of Newton method for high-dime...
Yuta Tsuboi, Yuya Unno, Hisashi Kashima, Naoaki Ok...
Recently, a number of researchers have proposed spectral algorithms for learning models of dynamical systems—for example, Hidden Markov Models (HMMs), Partially Observable Marko...