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24 papers · 1 filter
Learning Sparse Low-Threshold Linear Classifiers
Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro +2
We consider the problem of learning a non-negative linear classifier with a -norm of at most , and a fixed threshold, under the hinge-loss. This problem generalizes the probl…
Matrix reconstruction with the local max norm
Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov
We introduce a new family of matrix norms, the "local max" norms, generalizing existing methods such as the max norm, the trace norm (nuclear norm), and the weighted or smoothed we…
Tightening Fractional Covering Upper Bounds on the Partition Function for High-Order Region Graphs
Tamir Hazan, Jian Peng, Amnon Shashua
In this paper we present a new approach for tightening upper bounds on the partition function. Our upper bounds are based on fractional covering bounds on the entropy function, and…
Deterministic MDPs with Adversarial Rewards and Bandit Feedback
Raman Arora, Ofer Dekel, Ambuj Tewari
We consider a Markov decision process with deterministic state transition dynamics, adversarially generated rewards that change arbitrarily from round to round, and a bandit feedba…
An Efficient Message-Passing Algorithm for the M-Best MAP Problem
Dhruv Batra
Much effort has been directed at algorithms for obtaining the highest probability configuration in a probabilistic random field model known as the maximum a posteriori (MAP) infere…
Case-Factor Diagrams for Structured Probabilistic Modeling
David A. McAllester, Michael Collins, Fernando Pereira
We introduce a probabilistic formalism subsuming Markov random fields of bounded tree width and probabilistic context free grammars. Our models are based on a representation of Boo…