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Suvrit Sra

Suvrit Sra

D-Index & Metrics

Engineering and Technology

D-Index
63
Citations
14813
World Ranking
1759
National Ranking
570

Overview

Suvrit Sra is affiliated with MIT in the United States and has contributed extensively to research in computer science and mathematics. Their scholarly work spans multiple subfields, with a focus on artificial intelligence, applied mathematics, computational mechanics, computational theory and mathematics, and numerical analysis.

Their research topics include:

  • Stochastic Gradient Optimization Techniques
  • Sparse and Compressive Sensing Techniques
  • Advanced Optimization Algorithms Research
  • Mathematical Inequalities and Applications
  • Optimization and Variational Analysis
  • Domain Adaptation and Few-Shot Learning
  • Point processes and geometric inequalities

Suvrit Sra has published in a variety of venues, with a significant number of papers appearing in arXiv (Cornell University). Their frequent publication venues include:

  • arXiv (Cornell University)
  • Linear Algebra and its Applications
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Mathematical Programming
  • Frontiers in Artificial Intelligence

Among recent papers authored or coauthored by Sra are:

  • "Max-Margin Contrastive Learning," 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Contrastive Learning with Hard Negative Samples," 2020, arXiv (Cornell University)
  • "Sign and Basis Invariant Networks for Spectral Graph Representation Learning," 2022, arXiv (Cornell University)
  • "Riemannian Optimization via Frank-Wolfe Methods," 2022, Mathematical Programming
  • "From Nesterov's Estimate Sequence to Riemannian Acceleration," 2020, arXiv (Cornell University)

Collaboration plays a role in Sra's research efforts. Frequent coauthors include:

  • Kwangjun Ahn
  • Alp Yurtsever
  • Jingzhao Zhang
  • Ali Jadbabaie
  • Chulhee Yun

Best Publications

  • Information-theoretic metric learning

    Jason V. Davis;Brian Kulis;Prateek Jain;Suvrit Sra

  • Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

    Arindam Banerjee;Inderjit S. Dhillon;Joydeep Ghosh;Suvrit Sra

  • Optimization for Machine Learning

    Suvrit Sra;Sebastian Nowozin;Stephen J. Wright

  • Generalized Nonnegative Matrix Approximations with Bregman Divergences

    Suvrit Sra;Inderjit S. Dhillon

  • Stochastic variance reduction for nonconvex optimization

    Sashank J. Reddi;Ahmed Hefny;Suvrit Sra;Barnabás Póczós

  • Minimum sum-squared residue co-clustering of gene expression data

    Hyuk Cho;Inderjit S. Dhillon;Yuqiang Guan;Suvrit Sra

  • Efficient filter flow for space-variant multiframe blind deconvolution

    Michael Hirsch;Suvrit Sra;Bernhard Scholkopf;Stefan Harmeling

  • Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity

    Jingzhao Zhang;Tianxing He;Suvrit Sra;Ali Jadbabaie

  • Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices

    A. Cherian;S. Sra;A. Banerjee;N. Papanikolopoulos

  • A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of I s ( x )

    Suvrit Sra

  • Randomized Nonlinear Component Analysis

    David Lopez-Paz;Suvrit Sra;Alex Smola;Zoubin Ghahramani

  • Entropic metric alignment for correspondence problems

    Justin Solomon;Gabriel Peyré;Vladimir G. Kim;Suvrit Sra

  • Randomized Nonlinear Component Analysis

    David Lopez-Paz;Suvrit Sra;Alex Smola;Zoubin Ghahramani

  • Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization

    Sashank J. Reddi;Suvrit Sra;Barnabas Poczos;Alexander J. Smola

  • Riemannian SVRG: fast stochastic optimization on riemannian manifolds

    Hongyi Zhang;Sashank J. Reddi;Suvrit Sra

  • First-order Methods for Geodesically Convex Optimization

    Hongyi Zhang;Suvrit Sra

  • On variance reduction in stochastic gradient descent and its asynchronous variants

    Sashank J. Reddi;Ahmed Hefny;Suvrit Sra;Barnabás Pöczos

  • Generative model-based clustering of directional data

    Arindam Banerjee;Inderjit Dhillon;Joydeep Ghosh;Suvrit Sra

  • Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem

    Dongmin Kim;Suvrit Sra;Inderjit S. Dhillon

  • Stochastic Frank-Wolfe methods for nonconvex optimization

    Sashank J. Reddi;Suvrit Sra;Barnabas Poczos;Alex Smola

  • Conic Geometric Optimization on the Manifold of Positive Definite Matrices

    Suvrit Sra;Reshad Hosseini

  • Fast stochastic optimization on Riemannian manifolds.

    Hongyi Zhang;Sashank J. Reddi;Suvrit Sra

Frequent Co-Authors

Inderjit S. Dhillon
Inderjit S. Dhillon Google (United States)
Sashank J. Reddi
Sashank J. Reddi Google (United States)
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Anoop Cherian
Anoop Cherian Mitsubishi Electric (United States)
Barnabás Póczos
Barnabás Póczos Carnegie Mellon University
Arindam Banerjee
Arindam Banerjee University of Illinois at Urbana-Champaign
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Sanjiv Kumar
Sanjiv Kumar Google (United States)

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