Sra, Suvrit

102 publications

NeurIPS 2025 Cross-Fluctuation Phase Transitions Reveal Sampling Dynamics in Diffusion Models Sai Niranjan Ramachandran, Manish Krishan Lal, Suvrit Sra

ICLR 2025 Graph Transformers Dream of Electric Flow Xiang Cheng, Lawrence Carin, Suvrit Sra

NeurIPS 2025 Revisiting Frank-Wolfe for Structured Nonconvex Optimization Hoomaan Maskan, Yikun Hou, Suvrit Sra, Alp Yurtsever

NeurIPS 2024 First-Order Methods for Linearly Constrained Bilevel Optimization Guy Kornowski, Swati Padmanabhan, Kai Wang, Jimmy Zhang, Suvrit Sra

ICML 2024 How to Escape Sharp Minima with Random Perturbations Kwangjun Ahn, Ali Jadbabaie, Suvrit Sra

ICLR 2024 Linear Attention Is (maybe) All You Need (to Understand Transformer Optimization) Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra

ICML 2024 Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions in Context Xiang Cheng, Yuxin Chen, Suvrit Sra

L4DC 2023 Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control? Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra

ICML 2023 Global Optimality for Euclidean CCCP Under Riemannian Convexity Melanie Weber, Suvrit Sra

NeurIPSW 2023 Linear Attention Is (maybe) All You Need (to Understand Transformer Optimization) Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra

ICML 2023 On the Training Instability of Shuffling SGD with Batch Normalization David Xing Wu, Chulhee Yun, Suvrit Sra

ICLR 2023 Sign and Basis Invariant Networks for Spectral Graph Representation Learning Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka

NeurIPSW 2023 Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm Peiyuan Zhang, Jingzhao Zhang, Suvrit Sra

NeurIPS 2023 The Crucial Role of Normalization in Sharpness-Aware Minimization Yan Dai, Kwangjun Ahn, Suvrit Sra

ICMLW 2023 Toward Understanding Latent Model Learning in MuZero: A Case Study in Linear Quadratic Gaussian Control Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra

NeurIPS 2023 Transformers Learn to Implement Preconditioned Gradient Descent for In-Context Learning Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, Suvrit Sra

ICML 2022 Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie

NeurIPS 2022 CCCP Is Frank-Wolfe in Disguise Alp Yurtsever, Suvrit Sra

NeurIPS 2022 Efficient Sampling on Riemannian Manifolds via Langevin MCMC Xiang Cheng, Jingzhao Zhang, Suvrit Sra

AAAI 2022 Max-Margin Contrastive Learning Anshul Shah, Suvrit Sra, Rama Chellappa, Anoop Cherian

ICLR 2022 Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond Chulhee Yun, Shashank Rajput, Suvrit Sra

ICML 2022 Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie

ICLRW 2022 Sign and Basis Invariant Networks for Spectral Graph Representation Learning Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka

L4DC 2022 Time Varying Regression with Hidden Linear Dynamics Horia Mania, Ali Jadbabaie, Devavrat Shah, Suvrit Sra

COLT 2022 Understanding Riemannian Acceleration via a Proximal Extragradient Framework Jikai Jin, Suvrit Sra

ICML 2022 Understanding the Unstable Convergence of Gradient Descent Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra

NeurIPS 2021 Can Contrastive Learning Avoid Shortcut Solutions? Joshua W. Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra

ICLR 2021 Contrastive Learning with Hard Negative Samples Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka

ICLR 2021 Coping with Label Shift via Distributionally Robust Optimisation Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra

ICML 2021 Online Learning in Unknown Markov Games Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra

COLT 2021 Open Problem: Can Single-Shuffle SGD Be Better than Reshuffling SGD and GD? Chulhee Yun, Suvrit Sra, Ali Jadbabaie

ICML 2021 Provably Efficient Algorithms for Multi-Objective Competitive RL Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra

NeurIPS 2021 Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates Alp Yurtsever, Alex Gu, Suvrit Sra

ICML 2021 Three Operator Splitting with a Nonconvex Loss Function Alp Yurtsever, Varun Mangalick, Suvrit Sra

ICML 2020 Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie

COLT 2020 From Nesterov’s Estimate Sequence to Riemannian Acceleration Kwangjun Ahn, Suvrit Sra

ACML 2020 Geodesically-Convex Optimization for Averaging Partially Observed Covariance Matrices Florian Yger, Sylvain Chevallier, Quentin Barthélemy, Suvrit Sra

ICML 2020 Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu

NeurIPS 2020 SGD with Shuffling: Optimal Rates Without Component Convexity and Large Epoch Requirements Kwangjun Ahn, Chulhee Yun, Suvrit Sra

ICML 2020 Strength from Weakness: Fast Learning Using Weak Supervision Joshua Robinson, Stefanie Jegelka, Suvrit Sra

NeurIPS 2020 Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes Yi Tian, Jian Qian, Suvrit Sra

NeurIPS 2020 Why Are Adaptive Methods Good for Attention Models? Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar, Suvrit Sra

ICLR 2020 Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie

NeurIPS 2019 Are Deep ResNets Provably Better than Linear Predictors? Chulhee Yun, Suvrit Sra, Ali Jadbabaie

ICML 2019 Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator Alp Yurtsever, Suvrit Sra, Volkan Cevher

ICLR 2019 Efficiently Testing Local Optimality and Escaping Saddles for ReLU Networks Chulhee Yun, Suvrit Sra, Ali Jadbabaie

ICML 2019 Escaping Saddle Points with Adaptive Gradient Methods Matthew Staib, Sashank Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra

NeurIPS 2019 Flexible Modeling of Diversity with Strongly Log-Concave Distributions Joshua Robinson, Suvrit Sra, Stefanie Jegelka

AISTATS 2019 Learning Determinantal Point Processes by Corrective Negative Sampling Zelda Mariet, Mike Gartrell, Suvrit Sra

ICML 2019 Random Shuffling Beats SGD After Finite Epochs Jeff Haochen, Suvrit Sra

ICLR 2019 Small Nonlinearities in Activation Functions Create Bad Local Minima in Neural Networks Chulhee Yun, Suvrit Sra, Ali Jadbabaie

NeurIPS 2019 Small ReLU Networks Are Powerful Memorizers: A Tight Analysis of Memorization Capacity Chulhee Yun, Suvrit Sra, Ali Jadbabaie

AISTATS 2018 A Generic Approach for Escaping Saddle Points Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola

COLT 2018 An Estimate Sequence for Geodesically Convex Optimization Hongyi Zhang, Suvrit Sra

NeurIPS 2018 Direct Runge-Kutta Discretization Achieves Acceleration Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie

NeurIPS 2018 Exponentiated Strongly Rayleigh Distributions Zelda E. Mariet, Suvrit Sra, Stefanie Jegelka

ICLR 2018 Global Optimality Conditions for Deep Neural Networks Chulhee Yun, Suvrit Sra, Ali Jadbabaie

JMLR 2018 Modular Proximal Optimization for Multidimensional Total-Variation Regularization Alvaro Barbero, Suvrit Sra

AISTATS 2017 Combinatorial Topic Models Using Small-Variance Asymptotics Ke Jiang, Suvrit Sra, Brian Kulis

NeurIPS 2017 Elementary Symmetric Polynomials for Optimal Experimental Design Zelda E. Mariet, Suvrit Sra

NeurIPS 2017 Polynomial Time Algorithms for Dual Volume Sampling Chengtao Li, Stefanie Jegelka, Suvrit Sra

AISTATS 2016 AdaDelay: Delay Adaptive Distributed Stochastic Optimization Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola

ICLR 2016 Diversity Networks Zelda Mariet, Suvrit Sra

AISTATS 2016 Efficient Sampling for K-Determinantal Point Processes Chengtao Li, Stefanie Jegelka, Suvrit Sra

ICML 2016 Fast DPP Sampling for Nystrom with Application to Kernel Methods Chengtao Li, Stefanie Jegelka, Suvrit Sra

NeurIPS 2016 Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling Chengtao Li, Suvrit Sra, Stefanie Jegelka

COLT 2016 First-Order Methods for Geodesically Convex Optimization Hongyi Zhang, Suvrit Sra

ICML 2016 Gaussian Quadrature for Matrix Inverse Forms with Applications Chengtao Li, Suvrit Sra, Stefanie Jegelka

ICML 2016 Geometric Mean Metric Learning Pourya Zadeh, Reshad Hosseini, Suvrit Sra

NeurIPS 2016 Kronecker Determinantal Point Processes Zelda E. Mariet, Suvrit Sra

ICML 2016 Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric Xing

NeurIPS 2016 Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alexander J Smola

NeurIPS 2016 Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds Hongyi Zhang, Sashank J. Reddi, Suvrit Sra

ICML 2016 Stochastic Variance Reduction for Nonconvex Optimization Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alex Smola

AISTATS 2015 Data Modeling with the Elliptical Gamma Distribution Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge

ICML 2015 Fixed-Point Algorithms for Learning Determinantal Point Processes Zelda Mariet, Suvrit Sra

UAI 2015 Large-Scale Randomized-Coordinate Descent Methods with Non-Separable Linear Constraints Sashank J. Reddi, Ahmed Hefny, Carlton Downey, Avinava Dubey, Suvrit Sra

NeurIPS 2015 Matrix Manifold Optimization for Gaussian Mixtures Reshad Hosseini, Suvrit Sra

NeurIPS 2015 On Variance Reduction in Stochastic Gradient Descent and Its Asynchronous Variants Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alexander J Smola

NeurIPS 2014 Efficient Structured Matrix Rank Minimization Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime Carbonell, Suvrit Sra

UAI 2014 Fast Newton Methods for the Group Fused Lasso Matt Wytock, Suvrit Sra, Jeremy Z. Kolter

ICML 2014 Randomized Nonlinear Component Analysis David Lopez-Paz, Suvrit Sra, Alex Smola, Zoubin Ghahramani, Bernhard Schoelkopf

ECCV 2014 Riemannian Sparse Coding for Positive Definite Matrices Anoop Cherian, Suvrit Sra

ICML 2014 Towards an Optimal Stochastic Alternating Direction Method of Multipliers Samaneh Azadi, Suvrit Sra

NeurIPS 2013 Geometric Optimisation on Positive Definite Matrices for Elliptically Contoured Distributions Suvrit Sra, Reshad Hosseini

NeurIPS 2013 Reflection Methods for User-Friendly Submodular Optimization Stefanie Jegelka, Francis Bach, Suvrit Sra

NeurIPS 2012 A New Metric on the Manifold of Kernel Matrices with Application to Matrix Geometric Means Suvrit Sra

NeurIPS 2012 Scalable Nonconvex Inexact Proximal Splitting Suvrit Sra

ICCV 2011 Efficient Similarity Search for Covariance Matrices via the Jensen-Bregman LogDet Divergence Anoop Cherian, Suvrit Sra, Arindam Banerjee, Nikolaos Papanikolopoulos

ICML 2011 Fast Newton-Type Methods for Total Variation Regularization Álvaro Barbero Jiménez, Suvrit Sra

ECML-PKDD 2011 Fast Projections onto ℓ1, Q -Norm Balls for Grouped Feature Selection Suvrit Sra

ECML-PKDD 2011 Generalized Dictionary Learning for Symmetric Positive Definite Matrices with Application to Nearest Neighbor Retrieval Suvrit Sra, Anoop Cherian

ICML 2010 A Scalable Trust-Region Algorithm with Application to Mixed-Norm Regression Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon

CVPR 2010 Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution Michael Hirsch, Suvrit Sra, Bernhard Schölkopf, Stefan Harmeling

ALT 2009 Approximation Algorithms for Tensor Clustering Stefanie Jegelka, Suvrit Sra, Arindam Banerjee

AISTATS 2009 Convex Perturbations for Scalable Semidefinite Programming Brian Kulis, Suvrit Sra, Inderjit Dhillon

ICML 2009 Workshop Summary: Numerical Mathematics in Machine Learning Matthias W. Seeger, Suvrit Sra, John P. Cunningham

ICML 2007 Information-Theoretic Metric Learning Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon

ECML-PKDD 2006 Efficient Large Scale Linear Programming Support Vector Machines Suvrit Sra

JMLR 2005 Clustering on the Unit Hypersphere Using Von Mises-Fisher Distributions Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra

NeurIPS 2005 Generalized Nonnegative Matrix Approximations with Bregman Divergences Suvrit Sra, Inderjit S. Dhillon

NeurIPS 2004 Triangle Fixing Algorithms for the Metric Nearness Problem Suvrit Sra, Joel Tropp, Inderjit S. Dhillon