* 12 pages, slightly modified version of submitted book chapter

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* 24 pages with several new results; a fraction of this paper also appeared at the Neural Information Processing Systems (NIPS) Conference, Dec. 2012

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* revised version 12 pages, 2 figures; superset of shorter counterpart in NIPS 2012

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* Preprint of paper under review

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* Published in 31th Annual Conference on Learning Theory (COLT'18)

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Riemannian Frank-Wolfe with application to the geometric mean of positive definite matrices

May 09, 2018

Melanie Weber, Suvrit Sra

May 09, 2018

Melanie Weber, Suvrit Sra

* Under review; 21 pages, 2 figures

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Modular proximal optimization for multidimensional total-variation regularization

Dec 30, 2017

Álvaro Barbero, Suvrit Sra

Dec 30, 2017

Álvaro Barbero, Suvrit Sra

* 67 pages, 32 figures, new non-iterative fast TV algorithm, extensive new experiments, corresponds to the github proxtv repository now

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An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization

Jun 10, 2017

Reshad Hosseini, Suvrit Sra

Jun 10, 2017

Reshad Hosseini, Suvrit Sra

* 21 pages, 6 figures

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Diversity Networks: Neural Network Compression Using Determinantal Point Processes

Apr 18, 2017

Zelda Mariet, Suvrit Sra

Apr 18, 2017

Zelda Mariet, Suvrit Sra

* This paper appeared under the shorter title Diversity Networks at ICLR 2016 (http://www.iclr.cc/doku.php?id=iclr2016:main#accepted_papers_conference_track)

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* 21 pages

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Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices

Dec 17, 2015

Anoop Cherian, Suvrit Sra

Dec 17, 2015

Anoop Cherian, Suvrit Sra

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Fixed-point algorithms for learning determinantal point processes

Oct 08, 2015

Zelda Mariet, Suvrit Sra

Determinantal point processes (DPPs) offer an elegant tool for encoding probabilities over subsets of a ground set. Discrete DPPs are parametrized by a positive semidefinite matrix (called the DPP kernel), and estimating this kernel is key to learning DPPs from observed data. We consider the task of learning the DPP kernel, and develop for it a surprisingly simple yet effective new algorithm. Our algorithm offers the following benefits over previous approaches: (a) it is much simpler; (b) it yields equally good and sometimes even better local maxima; and (c) it runs an order of magnitude faster on large problems. We present experimental results on both real and simulated data to illustrate the numerical performance of our technique.
Oct 08, 2015

Zelda Mariet, Suvrit Sra

* ICML, 2015

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* 19 pages

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Statistical estimation for optimization problems on graphs

Nov 29, 2013

Mikhail Langovoy, Suvrit Sra

Large graphs abound in machine learning, data mining, and several related areas. A useful step towards analyzing such graphs is that of obtaining certain summary statistics - e.g., or the expected length of a shortest path between two nodes, or the expected weight of a minimum spanning tree of the graph, etc. These statistics provide insight into the structure of a graph, and they can help predict global properties of a graph. Motivated thus, we propose to study statistical properties of structured subgraphs (of a given graph), in particular, to estimate the expected objective function value of a combinatorial optimization problem over these subgraphs. The general task is very difficult, if not unsolvable; so for concreteness we describe a more specific statistical estimation problem based on spanning trees. We hope that our position paper encourages others to also study other types of graphical structures for which one can prove nontrivial statistical estimates.
Nov 29, 2013

Mikhail Langovoy, Suvrit Sra

* Paper for the NIPS Workshop on Discrete Optimization for Machine Learning (DISCML) (2011): Uncertainty, Generalization and Feedback

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Sparse Inverse Covariance Estimation via an Adaptive Gradient-Based Method

Jun 25, 2011

Suvrit Sra, Dongmin Kim

Jun 25, 2011

Suvrit Sra, Dongmin Kim

* 13 pages

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R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate

Nov 28, 2018

Jingzhao Zhang, Hongyi Zhang, Suvrit Sra

Nov 28, 2018

Jingzhao Zhang, Hongyi Zhang, Suvrit Sra

* arXiv admin note: text overlap with arXiv:1605.07147

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Flexible Modeling of Diversity with Strongly Log-Concave Distributions

Jun 12, 2019

Joshua Robinson, Suvrit Sra, Stefanie Jegelka

Jun 12, 2019

Joshua Robinson, Suvrit Sra, Stefanie Jegelka

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Learning Determinantal Point Processes by Sampling Inferred Negatives

Nov 02, 2018

Zelda Mariet, Mike Gartrell, Suvrit Sra

Nov 02, 2018

Zelda Mariet, Mike Gartrell, Suvrit Sra

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