Generative model-based clustering of directional data. - Arindam Banerjee 001; Inderjit S. Dhillon; Joydeep Ghosh; Suvrit Sra (KDD, 2003)

Triangle Fixing Algorithms for the Metric Nearness Problem. - Inderjit S. Dhillon; Suvrit Sra; Joel A. Tropp (NIPS, 2004)

Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data. - Hyuk Cho; Inderjit S. Dhillon; Yuqiang Guan; Suvrit Sra (SDM, 2004)

Generalized Nonnegative Matrix Approximations with Bregman Divergences. - Inderjit S. Dhillon; Suvrit Sra (NIPS, 2005)

Clustering on the Unit Hypersphere using von Mises-Fisher Distributions. - Arindam Banerjee 001; Inderjit S. Dhillon; Joydeep Ghosh; Suvrit Sra (J. Mach. Learn. Res., 2005)

Efficient Large Scale Linear Programming Support Vector Machines. - Suvrit Sra (ECML, 2006)

Row-Action Methods for Compressed Sensing. - Suvrit Sra; Joel A. Tropp (ICASSP (3), 2006)

Incremental Aspect Models for Mining Document Streams. - Arun C. Surendran; Suvrit Sra (PKDD, 2006)

Information-theoretic metric learning. - Jason V. Davis; Brian Kulis; Prateek Jain 002; Suvrit Sra; Inderjit S. Dhillon (ICML, 2007)

Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem. - Dongmin Kim; Suvrit Sra; Inderjit S. Dhillon (SDM, 2007)

Block-Iterative Algorithms for Non-negative Matrix Approximation. - Suvrit Sra (ICDM, 2008)

Approximation Algorithms for Bregman Co-clustering and Tensor Clustering - Stefanie Jegelka; Suvrit Sra; Arindam Banerjee 001 (CoRR, 2008)

Fast Projection-Based Methods for the Least Squares Nonnegative Matrix Approximation Problem. - Dongmin Kim; Suvrit Sra; Inderjit S. Dhillon (Stat. Anal. Data Min., 2008)

The Metric Nearness Problem. - Justin Brickell; Inderjit S. Dhillon; Suvrit Sra; Joel A. Tropp (SIAM J. Matrix Anal. Appl., 2008)

Approximation Algorithms for Tensor Clustering. - Stefanie Jegelka; Suvrit Sra; Arindam Banerjee 001 (ALT, 2009)

Workshop summary: Numerical mathematics in machine learning. - Matthias W. Seeger; Suvrit Sra; John P. Cunningham (ICML, 2009)

A Trivial Observation related to Sparse Recovery - Suvrit Sra (CoRR, 2009)

Convex Perturbations for Scalable Semidefinite Programming. - Brian Kulis; Suvrit Sra; Inderjit S. Dhillon (AISTATS, 2009)

Efficient filter flow for space-variant multiframe blind deconvolution. - Michael Hirsch 001; Suvrit Sra; Bernhard Schölkopf; Stefan Harmeling (CVPR, 2010)

Multiframe blind deconvolution, super-resolution, and saturation correction via incremental EM. - Stefan Harmeling; Suvrit Sra; Michael Hirsch 001; Bernhard Schölkopf (ICIP, 2010)

A scalable trust-region algorithm with application to mixed-norm regression. - Dongmin Kim; Suvrit Sra; Inderjit S. Dhillon (ICML, 2010)

Tackling Box-Constrained Optimization via a New Projected Quasi-Newton Approach. - Dongmin Kim; Suvrit Sra; Inderjit S. Dhillon (SIAM J. Sci. Comput., 2010)

Denoising sparse noise via online dictionary learning. - Anoop Cherian; Suvrit Sra; Nikolaos Papanikolopoulos (ICASSP, 2011)

Efficient similarity search for covariance matrices via the Jensen-Bregman LogDet Divergence. - Anoop Cherian; Suvrit Sra; Arindam Banerjee 001; Nikolaos Papanikolopoulos (ICCV, 2011)

Fast Newton-type Methods for Total Variation Regularization. - Álvaro Barbero Jiménez; Suvrit Sra (ICML, 2011)

Fast Projections onto ℓ1, q -Norm Balls for Grouped Feature Selection. - Suvrit Sra (ECML/PKDD (3), 2011)

Generalized Dictionary Learning for Symmetric Positive Definite Matrices with Application to Nearest Neighbor Retrieval. - Suvrit Sra; Anoop Cherian (ECML/PKDD (3), 2011)

A new metric on the manifold of kernel matrices with application to matrix geometric means. - Suvrit Sra (NIPS, 2012)

Scalable nonconvex inexact proximal splitting. - Suvrit Sra (NIPS, 2012)

A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of I s (x). - Suvrit Sra (Comput. Stat., 2012)

Fast projections onto mixed-norm balls with applications. - Suvrit Sra (Data Min. Knowl. Discov., 2012)

Reflection methods for user-friendly submodular optimization. - Stefanie Jegelka; Francis R. Bach; Suvrit Sra (NIPS, 2013)

Geometric optimisation on positive definite matrices for elliptically contoured distributions. - Suvrit Sra; Reshad Hosseini (NIPS, 2013)

Statistical estimation for optimization problems on graphs. - Mikhail A. Langovoy; Suvrit Sra (CoRR, 2013)

The multivariate Watson distribution: Maximum-likelihood estimation and other aspects. - Suvrit Sra; Dmitrii Karp (J. Multivar. Anal., 2013)

A non-monotonic method for large-scale non-negative least squares. - Dongmin Kim; Suvrit Sra; Inderjit S. Dhillon (Optim. Methods Softw., 2013)

Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices. - Anoop Cherian; Suvrit Sra; Arindam Banerjee 001; Nikolaos Papanikolopoulos (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Tractable Optimization in Machine Learning. - Suvrit Sra (Tractability, 2014)

Riemannian Sparse Coding for Positive Definite Matrices. - Anoop Cherian; Suvrit Sra (ECCV (3), 2014)

Towards an optimal stochastic alternating direction method of multipliers. - Samaneh Azadi; Suvrit Sra (ICML, 2014)

Randomized Nonlinear Component Analysis. - David Lopez-Paz; Suvrit Sra; Alexander J. Smola; Zoubin Ghahramani; Bernhard Schölkopf (ICML, 2014)

Efficient Structured Matrix Rank Minimization. - Adams Wei Yu; Wanli Ma; Yaoliang Yu; Jaime G. Carbonell; Suvrit Sra (NIPS, 2014)

Fast Newton methods for the group fused lasso. - Matt Wytock; Suvrit Sra; Jeremy Z. Kolter (UAI, 2014)

Efficient Nearest Neighbors via Robust Sparse Hashing. - Anoop Cherian; Suvrit Sra; Vassilios Morellas; Nikolaos Papanikolopoulos (IEEE Trans. Image Process., 2014)

Data modeling with the elliptical gamma distribution. - Suvrit Sra; Reshad Hosseini; Lucas Theis; Matthias Bethge (AISTATS, 2015)

Fixed-point algorithms for learning determinantal point processes. - Zelda Mariet; Suvrit Sra (ICML, 2015)

Matrix Manifold Optimization for Gaussian Mixtures. - Reshad Hosseini; Suvrit Sra (NIPS, 2015)

On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. - Sashank J. Reddi; Ahmed Hefny; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (NIPS, 2015)

Large-scale randomized-coordinate descent methods with non-separable linear constraints. - Sashank J. Reddi; Ahmed Hefny; Carlton Downey; Avinava Dubey; Suvrit Sra (UAI, 2015)

Manifold Optimization for Gaussian Mixture Models. - Reshad Hosseini; Suvrit Sra (CoRR, 2015)

Convex Optimization for Parallel Energy Minimization. - K. S. Sesh Kumar; Álvaro Barbero Jiménez; Stefanie Jegelka; Suvrit Sra; Francis R. Bach (CoRR, 2015)

Bounds on bilinear inverse forms via Gaussian quadrature with applications. - Chengtao Li; Suvrit Sra; Stefanie Jegelka (CoRR, 2015)

Fixed-point algorithms for determinantal point processes. - Zelda Mariet; Suvrit Sra (CoRR, 2015)

AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization. - Suvrit Sra; Adams Wei Yu; Mu Li 003; Alexander J. Smola (CoRR, 2015)

Conic Geometric Optimization on the Manifold of Positive Definite Matrices. - Suvrit Sra; Reshad Hosseini (SIAM J. Optim., 2015)

Efficient Sampling for k-Determinantal Point Processes. - Chengtao Li; Stefanie Jegelka; Suvrit Sra (AISTATS, 2016)

AdaDelay: Delay Adaptive Distributed Stochastic Optimization. - Suvrit Sra; Adams Wei Yu; Mu Li 003; Alexander J. Smola (AISTATS, 2016)

Stochastic Frank-Wolfe methods for nonconvex optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (Allerton, 2016)

Fast incremental method for smooth nonconvex optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (CDC, 2016)

First-order Methods for Geodesically Convex Optimization. - Hongyi Zhang; Suvrit Sra (COLT, 2016)

Fast DPP Sampling for Nystrom with Application to Kernel Methods. - Chengtao Li; Stefanie Jegelka; Suvrit Sra (ICML, 2016)

Gaussian quadrature for matrix inverse forms with applications. - Chengtao Li; Suvrit Sra; Stefanie Jegelka (ICML, 2016)

Stochastic Variance Reduction for Nonconvex Optimization. - Sashank J. Reddi; Ahmed Hefny; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (ICML, 2016)

Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. - Yu-Xiang Wang 003; Veeranjaneyulu Sadhanala; Wei Dai 003; Willie Neiswanger; Suvrit Sra; Eric P. Xing (ICML, 2016)

Geometric Mean Metric Learning. - Pourya Zadeh; Reshad Hosseini; Suvrit Sra (ICML, 2016)

Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling. - Chengtao Li; Suvrit Sra; Stefanie Jegelka (NIPS, 2016)

Kronecker Determinantal Point Processes. - Zelda E. Mariet; Suvrit Sra (NIPS, 2016)

Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (NIPS, 2016)

Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds. - Hongyi Zhang; Sashank J. Reddi; Suvrit Sra (NIPS, 2016)

Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes. - Chengtao Li; Stefanie Jegelka; Suvrit Sra (CoRR, 2016)

Diversity Networks. - Zelda Mariet; Suvrit Sra (ICLR (Poster), 2016)

Fast Incremental Method for Nonconvex Optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (CoRR, 2016)

Fast Stochastic Methods for Nonsmooth Nonconvex Optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (CoRR, 2016)

Fast stochastic optimization on Riemannian manifolds. - Hongyi Zhang; Sashank J. Reddi; Suvrit Sra (CoRR, 2016)

Inference and mixture modeling with the Elliptical Gamma Distribution. - Reshad Hosseini; Suvrit Sra; Lucas Theis; Matthias Bethge (Comput. Stat. Data Anal., 2016)

On inequalities for normalized Schur functions. - Suvrit Sra (Eur. J. Comb., 2016)

Entropic metric alignment for correspondence problems. - Justin Solomon 001; Gabriel Peyré; Vladimir G. Kim; Suvrit Sra (ACM Trans. Graph., 2016)

Combinatorial Topic Models using Small-Variance Asymptotics. - Ke Jiang; Suvrit Sra; Brian Kulis (AISTATS, 2017)

Polynomial time algorithms for dual volume sampling. - Chengtao Li; Stefanie Jegelka; Suvrit Sra (NIPS, 2017)

Elementary Symmetric Polynomials for Optimal Experimental Design. - Zelda E. Mariet; Suvrit Sra (NIPS, 2017)

Sequence Summarization Using Order-constrained Kernelized Feature Subspaces. - Anoop Cherian; Suvrit Sra; Richard Hartley 001 (CoRR, 2017)

Unsupervised robust nonparametric learning of hidden community properties. - Mikhail A. Langovoy; Akhilesh Gotmare; Martin Jaggi; Suvrit Sra (CoRR, 2017)

Frank-Wolfe methods for geodesically convex optimization with application to the matrix geometric mean. - Melanie Weber 001; Suvrit Sra (CoRR, 2017)

Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices. - Anoop Cherian; Suvrit Sra (IEEE Trans. Neural Networks Learn. Syst., 2017)

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

On Geodesically Convex Formulations for the Brascamp-Lieb Constant. - Suvrit Sra; Nisheeth K. Vishnoi; Ozan Yildiz (APPROX-RANDOM, 2018)

An Estimate Sequence for Geodesically Convex Optimization. - Hongyi Zhang; Suvrit Sra (COLT, 2018)

Non-Linear Temporal Subspace Representations for Activity Recognition. - Anoop Cherian; Suvrit Sra; Stephen Gould; Richard Hartley 001 (CVPR, 2018)

Distributional Adversarial Networks. - Chengtao Li; David Alvarez-Melis; Keyulu Xu; Stefanie Jegelka; Suvrit Sra (ICLR (Workshop), 2018)

Global Optimality Conditions for Deep Neural Networks. - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (ICLR (Poster), 2018)

Exponentiated Strongly Rayleigh Distributions. - Zelda E. Mariet; Suvrit Sra; Stefanie Jegelka (NeurIPS, 2018)

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

A Critical View of Global Optimality in Deep Learning. - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (CoRR, 2018)

Learning Determinantal Point Processes by Sampling Inferred Negatives. - Zelda Mariet; Mike Gartrell; Suvrit Sra (CoRR, 2018)

Towards Riemannian Accelerated Gradient Methods. - Hongyi Zhang; Suvrit Sra (CoRR, 2018)

Finite sample expressive power of small-width ReLU networks. - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (CoRR, 2018)

R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate. - Jingzhao Zhang; Hongyi Zhang; Suvrit Sra (CoRR, 2018)

Deep-RBF Networks Revisited: Robust Classification with Rejection. - Pourya Habib Zadeh; Reshad Hosseini; Suvrit Sra (CoRR, 2018)

Modular Proximal Optimization for Multidimensional Total-Variation Regularization. - Álvaro Barbero Jiménez; Suvrit Sra (J. Mach. Learn. Res., 2018)

Learning Determinantal Point Processes by Corrective Negative Sampling. - Zelda Mariet; Mike Gartrell; Suvrit Sra (AISTATS, 2019)

Acceleration in First Order Quasi-strongly Convex Optimization by ODE Discretization. - Jingzhao Zhang; Suvrit Sra; Ali Jadbabaie (CDC, 2019)

Efficiently testing local optimality and escaping saddles for ReLU networks. - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (ICLR (Poster), 2019)

Small nonlinearities in activation functions create bad local minima in neural networks. - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (ICLR (Poster), 2019)

Random Shuffling Beats SGD after Finite Epochs. - Jeff Z. HaoChen; Suvrit Sra (ICML, 2019)

Escaping Saddle Points with Adaptive Gradient Methods. - Matthew Staib; Sashank J. Reddi; Satyen Kale; Sanjiv Kumar; Suvrit Sra (ICML, 2019)

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

Flexible Modeling of Diversity with Strongly Log-Concave Distributions. - Joshua Robinson 001; Suvrit Sra; Stefanie Jegelka (NeurIPS, 2019)

Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity. - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (NeurIPS, 2019)

Are deep ResNets provably better than linear predictors? - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (NeurIPS, 2019)

Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition. - Jingzhao Zhang; Tianxing He; Suvrit Sra; Ali Jadbabaie (CoRR, 2019)

Nonconvex stochastic optimization on manifolds via Riemannian Frank-Wolfe methods. - Melanie Weber 001; Suvrit Sra (CoRR, 2019)

Metrics Induced by Quantum Jensen-Shannon-Renyí and Related Divergences. - Suvrit Sra (CoRR, 2019)

Why ADAM Beats SGD for Attention Models. - Jingzhao Zhang; Sai Praneeth Karimireddy; Andreas Veit; Seungyeon Kim 001; Sashank J. Reddi; Sanjiv Kumar; Suvrit Sra (CoRR, 2019)

Geodesically-convex optimization for averaging partially observed covariance matrices. - Florian Yger; Sylvain Chevallier; Quentin Barthélemy; Suvrit Sra (ACML, 2020)

From Nesterov's Estimate Sequence to Riemannian Acceleration. - Kwangjun Ahn; Suvrit Sra (COLT, 2020)

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

Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition. - Chi Jin 001; Tiancheng Jin; Haipeng Luo; Suvrit Sra; Tiancheng Yu (ICML, 2020)

Strength from Weakness: Fast Learning Using Weak Supervision. - Joshua Robinson 001; Stefanie Jegelka; Suvrit Sra (ICML, 2020)

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

SGD with shuffling: optimal rates without component convexity and large epoch requirements. - Kwangjun Ahn; Chulhee Yun; 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 001; Sashank J. Reddi; Sanjiv Kumar; Suvrit Sra (NeurIPS, 2020)

On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions. - Jingzhao Zhang; Hongzhou Lin; Suvrit Sra; Ali Jadbabaie (CoRR, 2020)

On Tight Convergence Rates of Without-replacement SGD. - Kwangjun Ahn; Suvrit Sra (CoRR, 2020)

Stochastic Optimization with Non-stationary Noise. - Jingzhao Zhang; Hongzhou Lin; Subhro Das; Suvrit Sra; Ali Jadbabaie (CoRR, 2020)

Provably Efficient Online Agnostic Learning in Markov Games. - Yi Tian; Yuanhao Wang 001; Tiancheng Yu; Suvrit Sra (CoRR, 2020)

Why do classifier accuracies show linear trends under distribution shift? - Horia Mania; Suvrit Sra (CoRR, 2020)

An Interpretable Predictive Model of Vaccine Utilization for Tanzania. - Ramkumar Hariharan; Johnna Sundberg; Giacomo Gallino; Ashley Schmidt; Drew Arenth; Suvrit Sra; Benjamin Fels (Frontiers Artif. Intell., 2020)

An alternative to EM for Gaussian mixture models: batch and stochastic Riemannian optimization. - Reshad Hosseini; Suvrit Sra (Math. Program., 2020)

Open Problem: Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (COLT, 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 (ICLR, 2021)

Online Learning in Unknown Markov Games. - Yi Tian; Yuanhao Wang 001; Tiancheng Yu; Suvrit Sra (ICML, 2021)

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

Three Operator Splitting with a Nonconvex Loss Function. - Alp Yurtsever; Varun Mangalick; Suvrit Sra (ICML, 2021)

Can contrastive learning avoid shortcut solutions? - Joshua Robinson 001; Li Sun 010; Ke Yu; Kayhan Batmanghelich; Stefanie Jegelka; Suvrit Sra (NeurIPS, 2021)

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

Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? - Chulhee Yun; Suvrit Sra; Ali Jadbabaie (CoRR, 2021)

On Convergence of Training Loss Without Reaching Stationary Points. - Jingzhao Zhang; Haochuan Li; Suvrit Sra; Ali Jadbabaie (CoRR, 2021)

A Riemannian Accelerated Proximal Extragradient Framework and its Implications. - Jikai Jin; Suvrit Sra (CoRR, 2021)

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

Understanding Riemannian Acceleration via a Proximal Extragradient Framework. - Jikai Jin; Suvrit Sra (COLT, 2022)

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

Understanding the unstable convergence of gradient descent. - Kwangjun Ahn; Jingzhao Zhang; 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 (ICML, 2022)

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

Time Varying Regression with Hidden Linear Dynamics. - Horia Mania; Ali Jadbabaie; Devavrat Shah; Suvrit Sra (L4DC, 2022)

Efficient Sampling on Riemannian Manifolds via Langevin MCMC. - Xiang Cheng; Jingzhao Zhang; Suvrit Sra (NeurIPS, 2022)

CCCP is Frank-Wolfe in disguise. - Alp Yurtsever; Suvrit Sra (NeurIPS, 2022)

Understanding Nesterov's Acceleration via Proximal Point Method. - Kwangjun Ahn; Suvrit Sra (SOSA, 2022)

Minimax in Geodesic Metric Spaces: Sion's Theorem and Algorithms. - Peiyuan Zhang; Jingzhao Zhang; Suvrit Sra (CoRR, 2022)

On a class of geodesically convex optimization problems solved via Euclidean MM methods. - Suvrit Sra; Melanie Weber 001 (CoRR, 2022)

Computing Brascamp-Lieb Constants through the lens of Thompson Geometry. - Melanie Weber 001; Suvrit Sra (CoRR, 2022)

Toward Understanding State Representation Learning in MuZero: A Case Study in Linear Quadratic Gaussian Control. - Yi Tian; Kaiqing Zhang; Russ Tedrake; Suvrit Sra (CDC, 2023)

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

Global optimality for Euclidean CCCP under Riemannian convexity. - Melanie Weber 001; Suvrit Sra (ICML, 2023)

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

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

The Crucial Role of Normalization in Sharpness-Aware Minimization. - Yan Dai 002; Kwangjun Ahn; Suvrit Sra (NeurIPS, 2023)

Transformers learn to implement preconditioned gradient descent for in-context learning. - Kwangjun Ahn; Xiang Cheng; Hadi Daneshmand; Suvrit Sra (NeurIPS, 2023)

How to escape sharp minima. - Kwangjun Ahn; Ali Jadbabaie; Suvrit Sra (CoRR, 2023)

Invex Programs: First Order Algorithms and Their Convergence. - Adarsh Barik; Suvrit Sra; Jean Honorio (CoRR, 2023)

Riemannian Optimization via Frank-Wolfe Methods. - Melanie Weber 001; Suvrit Sra (Math. Program., 2023)

Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm. - Peiyuan Zhang; Jingzhao Zhang; Suvrit Sra (SIAM J. Optim., 2023)

Linear attention is (maybe) all you need (to understand Transformer optimization). - Kwangjun Ahn; Xiang Cheng; Minhak Song; Chulhee Yun; Ali Jadbabaie; Suvrit Sra (ICLR, 2024)

How to Escape Sharp Minima with Random Perturbations. - Kwangjun Ahn; Ali Jadbabaie; Suvrit Sra (ICML, 2024)

Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context. - Xiang Cheng; Yuxin Chen; Suvrit Sra (ICML, 2024)

First-Order Methods for Linearly Constrained Bilevel Optimization. - Guy Kornowski; Swati Padmanabhan; Kai Wang; Zhe Zhang; Suvrit Sra (NeurIPS, 2024)

Riemannian Bilevel Optimization. - Sanchayan Dutta; Xiang Cheng; Suvrit Sra (CoRR, 2024)

Memory-augmented Transformers can implement Linear First-Order Optimization Methods. - Sanchayan Dutta; Suvrit Sra (CoRR, 2024)

Graph Transformers Dream of Electric Flow. - Xiang Cheng; Lawrence Carin; Suvrit Sra (ICLR, 2025)

Implicit Bias in Matrix Factorization and its Explicit Realization in a New Architecture. - Yikun Hou; Suvrit Sra; Alp Yurtsever (CoRR, 2025)

Revisiting Frank-Wolfe for Structured Nonconvex Optimization. - Hoomaan Maskan; Yikun Hou; Suvrit Sra; Alp Yurtsever (CoRR, 2025)

A projection-based framework for gradient-free and parallel learning. - Andreas Bergmeister; Manish Krishan Lal; Stefanie Jegelka; Suvrit Sra (CoRR, 2025)

Linearly Convergent Algorithms for Nonsmooth Problems with Unknown Smooth Pieces. - Zhe Zhang; Suvrit Sra (CoRR, 2025)