Suvrit Sra

Q: What Schools Are Affiliated With Suvrit Sra

Suvrit Sra is affiliated with the following schools: University of Texas at Austin, Massachusetts Institute of Technology, IIT Bombay

Suvrit Sra's AcademicInfluence.com Rankings

Suvrit Sra

Engineering

#6090

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#7394

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Electrical Engineering

#1766

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#1865

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engineering Degrees

Suvrit Sra

Computer Science

#7927

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#8343

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Theoretical Computer Science

#100

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#100

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Data Science

#170

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#173

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Algorithms

#312

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#316

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computer-science Degrees

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Engineering
Computer Science

Suvrit Sra's Degrees

Bachelors Computer Science and Engineering IIT Bombay

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Suvrit Sra's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Information-theoretic metric learning (2006) (1931)
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions (2005) (888)
Stochastic Variance Reduction for Nonconvex Optimization (2016) (510)
Generalized Nonnegative Matrix Approximations with Bregman Divergences (2005) (484)
Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data (2004) (328)
Contrastive Learning with Hard Negative Samples (2020) (306)
Efficient filter flow for space-variant multiframe blind deconvolution (2010) (236)
First-order Methods for Geodesically Convex Optimization (2016) (214)
Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity (2019) (207)
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants (2015) (191)
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds (2016) (175)
Randomized Nonlinear Component Analysis (2014) (168)
Optimization for Machine Learning (2013) (165)
Entropic metric alignment for correspondence problems (2016) (164)
Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices (2013) (159)
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization (2016) (158)
Geometric Mean Metric Learning (2016) (140)
Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem (2007) (139)
A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of Is(x) (2012) (137)
Conic Geometric Optimization on the Manifold of Positive Definite Matrices (2013) (136)
A new metric on the manifold of kernel matrices with application to matrix geometric means (2012) (134)
Generative model-based clustering of directional data (2003) (122)
Stochastic Frank-Wolfe methods for nonconvex optimization (2016) (120)
Global optimality conditions for deep neural networks (2017) (119)
Matrix Differential Calculus (2005) (117)
Positive definite matrices and the S-divergence (2011) (110)
Why are Adaptive Methods Good for Attention Models? (2020) (108)
Modular Proximal Optimization for Multidimensional Total-Variation Regularization (2014) (105)
Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices (2015) (105)
Modeling Data using Directional Distributions (2003) (100)
Direct Runge-Kutta Discretization Achieves Acceleration (2018) (96)
Tackling Box-Constrained Optimization via a New Projected Quasi-Newton Approach (2010) (91)
Fast Newton-type Methods for Total Variation Regularization (2011) (87)
Small nonlinearities in activation functions create bad local minima in neural networks (2018) (84)
A Generic Approach for Escaping Saddle points (2017) (81)
Projected Newton-type methods in machine learning (2011) (79)
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity (2018) (79)
Matrix Manifold Optimization for Gaussian Mixtures (2015) (78)
Reflection methods for user-friendly submodular optimization (2013) (77)
Efficient similarity search for covariance matrices via the Jensen-Bregman LogDet Divergence (2011) (77)
Fast DPP Sampling for Nystrom with Application to Kernel Methods (2016) (76)
Nonnegative Matrix Approximation: Algorithms and Applications (2006) (75)
The multivariate Watson distribution: Maximum-likelihood estimation and other aspects (2011) (73)
Random Shuffling Beats SGD after Finite Epochs (2018) (69)
Positive definite matrices and the Symmetric Stein Divergence (2011) (65)
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition (2020) (64)
Scalable nonconvex inexact proximal splitting (2012) (64)
Fast incremental method for smooth nonconvex optimization (2016) (64)
A non-monotonic method for large-scale non-negative least squares (2013) (63)
Why ADAM Beats SGD for Attention Models (2019) (62)
Escaping Saddle Points with Adaptive Gradient Methods (2019) (61)
Diversity Networks: Neural Network Compression Using Determinantal Point Processes (2015) (60)
Towards an optimal stochastic alternating direction method of multipliers (2014) (60)
Diversity Networks (2015) (56)
Riemannian Sparse Coding for Positive Definite Matrices (2014) (55)
Can contrastive learning avoid shortcut solutions? (2021) (55)
From Nesterov's Estimate Sequence to Riemannian Acceleration (2020) (55)
Multiframe blind deconvolution, super-resolution, and saturation correction via incremental EM (2010) (53)
The Metric Nearness Problem (2008) (53)
Fast Stochastic Methods for Nonsmooth Nonconvex Optimization (2016) (51)
Efficient Sampling for k-Determinantal Point Processes (2015) (51)
Generalized Dictionary Learning for Symmetric Positive Definite Matrices with Application to Nearest Neighbor Retrieval (2011) (50)
Online blind deconvolution for astronomical imaging (2009) (50)
Online Multi-frame Blind Deconvolution with Super-resolution and Saturation Correction (2011) (48)
An alternative to EM for Gaussian mixture models: batch and stochastic Riemannian optimization (2017) (47)
Approximation Algorithms for Tensor Clustering (2009) (46)
An Estimate Sequence for Geodesically Convex Optimization (2018) (45)
Fixed-point algorithms for learning determinantal point processes (2015) (44)
Fast Incremental Method for Nonconvex Optimization (2016) (43)
Minimum Sum-Squared Residue based clustering of Gene Expression Data (2004) (42)
Sign and Basis Invariant Networks for Spectral Graph Representation Learning (2022) (41)
A scalable trust-region algorithm with application to mixed-norm regression (2010) (41)
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator (2019) (40)
Non-linear Temporal Subspace Representations for Activity Recognition (2018) (39)
Towards Riemannian Accelerated Gradient Methods (2018) (39)
Fast Projection‐Based Methods for the Least Squares Nonnegative Matrix Approximation Problem (2008) (39)
Fast Projections onto ℓ1, q -Norm Balls for Grouped Feature Selection (2011) (39)
Directional Statistics in Machine Learning: A Brief Review Suvrit Sra (2016) (38)
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms (2014) (38)
A New Projected Quasi-Newton Approach for the Nonnegative Least Squares Problem (2006) (36)
Fast stochastic optimization on Riemannian manifolds (2016) (36)
On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions (2020) (35)
Sparse nonnegative matrix approximation: new formulations and algorithms (2010) (34)
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling (2016) (34)
R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate (2018) (33)
Coping with Label Shift via Distributionally Robust Optimisation (2020) (33)
Online Learning in Unknown Markov Games (2021) (32)
SGD with shuffling: optimal rates without component convexity and large epoch requirements (2020) (32)
Polynomial time algorithms for dual volume sampling (2017) (32)
A Critical View of Global Optimality in Deep Learning (2018) (31)
AdaDelay: Delay Adaptive Distributed Stochastic Optimization (2016) (31)
Geometric optimisation on positive definite matrices for elliptically contoured distributions (2013) (29)
Fast projections onto mixed-norm balls with applications (2012) (29)
AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization (2015) (29)
Kronecker Determinantal Point Processes (2016) (26)
Strength from Weakness: Fast Learning Using Weak Supervision (2020) (25)
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes (2020) (22)
Deep-RBF Networks Revisited: Robust Classification with Rejection (2018) (22)
Large-scale randomized-coordinate descent methods with non-separable linear constraints (2014) (21)
Fast Projection-Based Methods for the Least Squares Nonnegative Matrix Approximation Problem (2008) (21)
Non-monotonic Poisson Likelihood Maximization (2008) (21)
Efficient Nearest Neighbors via Robust Sparse Hashing (2014) (21)
Efficient Structured Matrix Rank Minimization (2014) (20)
Triangle Fixing Algorithms for the Metric Nearness Problem (2004) (20)
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond (2021) (20)
Elementary Symmetric Polynomials for Optimal Experimental Design (2017) (20)
Understanding the unstable convergence of gradient descent (2022) (19)
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions (2020) (19)
Efficient Large Scale Linear Programming Support Vector Machines (2006) (19)
Incremental Aspect Models for Mining Document Streams (2006) (18)
Fast Newton methods for the group fused lasso (2014) (18)
Row-Action Methods for Compressed Sensing (2006) (18)
On the matrix square root via geometric optimization (2015) (18)
Geometric Optimization in Machine Learning (2016) (18)
The sum of squared logarithms inequality in arbitrary dimensions (2015) (17)
Gaussian quadrature for matrix inverse forms with applications (2015) (15)
Distributional Adversarial Networks (2017) (15)
Denoising sparse noise via online dictionary learning (2011) (15)
Provably Efficient Algorithms for Multi-Objective Competitive RL (2021) (14)
Projection-free nonconvex stochastic optimization on Riemannian manifolds (2019) (14)
Nonconvex stochastic optimization on manifolds via Riemannian Frank-Wolfe methods (2019) (13)
Exponentiated Strongly Rayleigh Distributions (2018) (12)
Positive Definite Matrices: Data Representation and Applications to Computer Vision (2016) (12)
Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? (2021) (12)
Approximation Algorithms for Bregman Clustering Co-clustering and Tensor Clustering (2008) (11)
Flexible Modeling of Diversity with Strongly Log-Concave Distributions (2019) (11)
Metrics induced by Jensen-Shannon and related divergences on positive definite matrices (2019) (11)
Why do classifier accuracies show linear trends under distribution shift? (2020) (11)
Matrix nearness problems in data mining (2007) (11)
Hlawka-Popoviciu inequalities on positive definite tensors (2014) (11)
On inequalities for normalized Schur functions (2015) (11)
Are deep ResNets provably better than linear predictors? (2019) (11)
Fast algorithms for total-variation based optimization (2010) (10)
Introduction: Optimization and Machine Learning (2011) (10)
Nonconvex proximal splitting: batch and incremental algorithms (2011) (9)
Convex Perturbations for Scalable Semidefinite Programming (2009) (9)
Generalized Proximity and Projection with Norms and Mixed-norms (2010) (9)
Frank-Wolfe methods for geodesically convex optimization with application to the matrix geometric mean (2017) (9)
Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition (2019) (9)
Provably Efficient Online Agnostic Learning in Markov Games (2020) (9)
Recent Advances in Stochastic Riemannian Optimization (2020) (8)
Convex Optimization for Parallel Energy Minimization (2015) (8)
Efficiently testing local optimality and escaping saddles for ReLU networks (2018) (8)
Max-Margin Contrastive Learning (2021) (8)
Fixed-point algorithms for determinantal point processes (2015) (8)
Minimax in Geodesic Metric Spaces: Sion's Theorem and Algorithms (2022) (7)
Portfolio Optimization with Groupwise Selection (2014) (7)
Asynchronous Parallel Block-Coordinate Frank-Wolfe (2014) (7)
Riemannian Optimization via Frank-Wolfe Methods (2017) (7)
Correlation matrix nearness and completion under observation uncertainty (2015) (7)
A proof of Thompson’S determinantal inequality (2016) (6)
Data modeling with the elliptical gamma distribution (2015) (6)
Completely strong superadditivity of generalized matrix functions (2014) (6)
Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes (2016) (6)
Block-Iterative Algorithms for Non-negative Matrix Approximation (2008) (6)
Learning Determinantal Point Processes by Corrective Negative Sampling (2018) (6)
Modeling data using directional distributions: Part II (2007) (6)
Acceleration in First Order Quasi-strongly Convex Optimization by ODE Discretization (2019) (5)
Approximation Algorithms for Bregman Co-clustering and Tensor Clustering (2008) (5)
Geometric optimisation on positive definite matrices with application to elliptically contoured distributions (2013) (5)
Inference and mixture modeling with the Elliptical Gamma Distribution (2014) (5)
Scalable Semidefinite Programming using Convex Perturbations (2007) (5)
Finite sample expressive power of small-width ReLU networks (2018) (5)
Expectation Maximization for Clustering on Hyperspheres (2003) (5)
New concavity and convexity results for symmetric polynomials and their ratios (2018) (5)
On Tight Convergence Rates of Without-replacement SGD (2020) (4)
A THEORETICAL JUSTIFICATION FOR ADAPTIVITY (2020) (4)
An Interpretable Predictive Model of Vaccine Utilization for Tanzania (2020) (4)
Manifold Optimization for Gaussian Mixture Models (2015) (4)
Jensen-Bregman LogDet Divergence for Efficient Similarity Computations on Positive Definite Tensors (2012) (4)
Logarithmic inequalities under a symmetric polynomial dominance order (2017) (4)
Three Operator Splitting with a Nonconvex Loss Function (2021) (4)
Theory and Algorithms for Diffusion Processes on Riemannian Manifolds (2022) (3)
CCCP is Frank-Wolfe in disguise (2022) (3)
Column Subset Selection via Polynomial Time Dual Volume Sampling (2017) (3)
A Riemannian Accelerated Proximal Extragradient Framework and its Implications (2021) (3)
Learning Determinantal Point Processes by Sampling Inferred Negatives (2018) (3)
Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective (2021) (3)
A New Non-monotonic Gradient Projection Method for the Non-negative Least Squares Problem (2008) (3)
Understanding Riemannian Acceleration via a Proximal Extragradient Framework (2021) (2)
Efficient Policy Learning for Non-Stationary MDPs under Adversarial Manipulation (2019) (2)
A new non-monotonic algorithm for PET image reconstruction (2009) (2)
Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates (2021) (2)
The Metric Nearness Problem with Applications (2003) (2)
Stochastic Optimization with Non-stationary Noise (2020) (2)
Combinatorial Topic Models using Small-Variance Asymptotics (2016) (2)
Statistical inference with the Elliptical Gamma Distribution (2014) (2)
Geodesically-convex optimization for averaging partially observed covariance matrices (2020) (2)
Optimal Batch Variance with Second-Order Marginals (2020) (2)
Unsupervised robust nonparametric learning of hidden community properties (2017) (2)
Text Clustering with Mixture of von Mises-Fisher Distributions (2009) (2)
Markov Chain Sampling in Discrete Probabilistic Models with Constraints (2016) (2)
Understanding Nesterov's Acceleration via Proximal Point Method (2020) (2)
Positive Definite Matrices: Symmetric positive definite (SPD) matrices Data RepresentationData representation and Applications to Computer Vision (2016) (2)
Near Optimal Stratified Sampling (2019) (2)
On Convergence of Training Loss Without Reaching Stationary Points (2021) (2)
On a class of geodesically convex optimization problems solved via Euclidean MM methods (2022) (2)
Riemannian Frank-Wolfe with application to the geometric mean of positive definite matrices (2017) (1)
Logarithmic inequalities under an elementary symmetric polynomial dominance order (2015) (1)
Metrics Induced by Quantum Jensen-Shannon-Renyí and Related Divergences (2019) (1)
A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of Is(x) (2011) (1)
Bounds on bilinear inverse forms via Gaussian quadrature with applications (2015) (1)
Solving large-scale nonnegative least squares using an adaptive non-monotonic method (2010) (1)
Computing Brascamp-Lieb Constants through the lens of Thompson Geometry (2022) (1)
An Incremental GEM Framework for Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction (2009) (1)
Inequalities via symmetric polynomial majorization (2015) (1)
Positive definite functions of noncommuting contractions, Hua-Bellman matrices, and a new distance metric (2021) (1)
Sparse Inverse Covariance Estimation via an Adaptive Gradient-Based Method (2011) (1)
Time Varying Regression with Hidden Linear Dynamics (2021) (1)
Explicit diagonalization of an anti-triangular Cesar\'o matrix (2014) (1)
Tractable Optimization in Machine Learning (2014) (1)
Sequence Summarization Using Order-constrained Kernelized Feature Subspaces (2017) (1)
Modeling data using directional distributions : Part II Suvrit Sra (2007) (0)
Efficient Sampling on Riemannian Manifolds via Langevin MCMC (2022) (0)
Spurious local minima in neural networks: a critical view (2018) (0)
Sparse regression via a trust-region proximal method (2010) (0)
HARD NEGATIVE SAMPLES (2021) (0)
Recent (2021) (0)
Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity (2020) (0)
New Projected Quasi-Newton Methods with Applications (2008) (0)
Fast projections onto mixed-norm balls with applications (2012) (0)
Non-monotonic Poisson (2008) (0)
Explicit eigenvalues of certain scaled trigonometric matrices (2012) (0)
An alternative to EM for Gaussian mixture models: batch and stochastic Riemannian optimization (2019) (0)
LOBAL OPTIMALITY CONDITIONS FOR DEEP NEURAL NETWORKS (2018) (0)
Introducing Discrepancy Values of Matrices with Application to Bounding Norms of Commutators (2021) (0)
Nonconvex proximal splitting with computational errors ∗ (2016) (0)
Statement of Research (2002) (0)
On the Training Instability of Shuffling SGD with Batch Normalization (2023) (0)
Sparse Recovery without the Restricted Isometry Property (2009) (0)
Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control? (2022) (0)
The Hornich-Hlawka functional inequality for functions with positive differences (2023) (0)
Statistical estimation for optimization problems on graphs (2011) (0)
Matrix Approximation Problems (2010) (0)
Solving large-scale nonnegative least-squares (2010) (0)
Supplementary Material for Combinatorial Topic Models using Small-Variance Asymptotics (2017) (0)
A proof of Thompson’S determinantal inequality (2016) (0)
Workshop summary: Numerical mathematics in machine learning (2009) (0)
A Trivial Observation related to Sparse Recovery (2009) (0)
Generalized Hlawka inequalities on positive definite tensors (2014) (0)

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What Schools Are Affiliated With Suvrit Sra?

Suvrit Sra is affiliated with the following schools:

Suvrit Sra's Academic­Influence.com Rankings

Suvrit Sra's Degrees

Why Is Suvrit Sra Influential?

Suvrit Sra's Published Works

Published Works

What Schools Are Affiliated With Suvrit Sra?

Suvrit Sra's AcademicInfluence.com Rankings