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Dive into the research topics where James R. Voss is active.

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Featured researches published by James R. Voss.


International Journal of Parallel, Emergent and Distributed Systems | 2014

Matching preclusion and conditional matching preclusion for pancake and burnt pancake graphs

Eddie Cheng; Philip Hu; Roger Jia; László Lipták; Brian Scholten; James R. Voss

The matching preclusion number of a graph with an even number of vertices is the minimum number of edges whose deletion destroys all perfect matchings in the graph. The optimal matching preclusion sets are often precisely those which are induced by a single vertex of minimum degree. To look for obstruction sets beyond these, the conditional matching preclusion number was introduced, which is defined similarly with the additional restriction that the resulting graph has no isolated vertices. In this paper we find the matching preclusion and conditional matching preclusion numbers and classify all optimal sets for the pancake graphs and burnt pancake graphs.


SIAM Journal on Computing | 2018

Eigenvectors of Orthogonally Decomposable Functions

Mikhail Belkin; Luis Rademacher; James R. Voss

The eigendecomposition of quadratic forms (symmetric matrices) guaranteed by the spectral theorem is a foundational result in applied mathematics. Motivated by a shared structure found in inferenti...


conference on learning theory | 2014

The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures

Joseph Anderson; Mikhail Belkin; Navin Goyal; Luis Rademacher; James R. Voss


conference on learning theory | 2013

Blind Signal Separation in the Presence of Gaussian Noise

Mikhail Belkin; Luis Rademacher; James R. Voss


neural information processing systems | 2013

Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis

James R. Voss; Luis Rademacher; Mikhail Belkin


national conference on artificial intelligence | 2016

The hidden convexity of spectral clustering

James R. Voss; Mikhail Belkin; Luis Rademacher


conference on learning theory | 2016

Basis Learning as an Algorithmic Primitive

Mikhail Belkin; Luis Rademacher; James R. Voss


Archive | 2014

Learning a Hidden Basis Through Imperfect Measurements: An Algorithmic Primitive.

Mikhail Belkin; Luis Rademacher; James R. Voss


Archive | 2016

Hidden Basis Recovery: Methods and Applications in Machine Learning

James R. Voss


neural information processing systems | 2015

A pseudo-Euclidean iteration for optimal recovery in noisy ICA

James R. Voss; Mikhail Belkin; Luis Rademacher

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Eddie Cheng

University of Rochester

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Roger Jia

Massachusetts Institute of Technology

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