| 2025 | ICLR | Graph Transformers Dream of Electric Flow. | Xiang Cheng, Lawrence Carin, Suvrit Sra |
| 2024 | ICLR | Linear attention is (maybe) all you need (to understand Transformer optimization). | Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra |
| 2024 | ICML | How to Escape Sharp Minima with Random Perturbations. | Kwangjun Ahn, Ali Jadbabaie, Suvrit Sra |
| 2024 | ICML | Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context. | Xiang Cheng, Yuxin Chen, Suvrit Sra |
| 2023 | ICLR | 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 |
| 2023 | ICML | Global optimality for Euclidean CCCP under Riemannian convexity. | Melanie Weber, Suvrit Sra |
| 2023 | ICML | On the Training Instability of Shuffling SGD with Batch Normalization. | David Xing Wu, Chulhee Yun, Suvrit Sra |
| 2022 | AAAI | Max-Margin Contrastive Learning. | Anshul Shah, Suvrit Sra, Rama Chellappa, Anoop Cherian |
| 2022 | COLT | Understanding Riemannian Acceleration via a Proximal Extragradient Framework. | Jikai Jin, Suvrit Sra |
| 2022 | ICLR | Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. | Chulhee Yun, Shashank Rajput, Suvrit Sra |
| 2022 | ICML | Understanding the unstable convergence of gradient descent. | Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra |
| 2022 | ICML | Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity. | Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie |
| 2022 | ICML | Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective. | Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie |
| 2021 | COLT | Open Problem: Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? | Chulhee Yun, Suvrit Sra, Ali Jadbabaie |
| 2021 | ICLR | Contrastive Learning with Hard Negative Samples. | Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka |
| 2021 | ICLR | Coping with Label Shift via Distributionally Robust Optimisation. | Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra |
| 2021 | ICML | Online Learning in Unknown Markov Games. | Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra |
| 2021 | ICML | Three Operator Splitting with a Nonconvex Loss Function. | Alp Yurtsever, Varun Mangalick, Suvrit Sra |
| 2021 | ICML | Provably Efficient Algorithms for Multi-Objective Competitive RL. | Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra |
| 2020 | ACML | Geodesically-convex optimization for averaging partially observed covariance matrices. | Florian Yger, Sylvain Chevallier, Quentin Barthlemy, Suvrit Sra |
| 2020 | COLT | From Nesterov's Estimate Sequence to Riemannian Acceleration. | Kwangjun Ahn, Suvrit Sra |
| 2020 | ICLR | Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity. | Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie |
| 2020 | ICML | Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition. | Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu |
| 2020 | ICML | Strength from Weakness: Fast Learning Using Weak Supervision. | Joshua Robinson, Stefanie Jegelka, Suvrit Sra |
| 2020 | ICML | Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. | Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie |
| 2019 | AISTATS | Learning Determinantal Point Processes by Corrective Negative Sampling. | Zelda Mariet, Mike Gartrell, Suvrit Sra |
| 2019 | ICLR | Efficiently testing local optimality and escaping saddles for ReLU networks. | Chulhee Yun, Suvrit Sra, Ali Jadbabaie |
| 2019 | ICLR | Small nonlinearities in activation functions create bad local minima in neural networks. | Chulhee Yun, Suvrit Sra, Ali Jadbabaie |
| 2019 | ICML | Random Shuffling Beats SGD after Finite Epochs. | Jeff Z. HaoChen, Suvrit Sra |
| 2019 | ICML | Escaping Saddle Points with Adaptive Gradient Methods. | Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra |
| 2019 | ICML | Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator. | Alp Yurtsever, Suvrit Sra, Volkan Cevher |
| 2018 | AISTATS | A Generic Approach for Escaping Saddle points. | Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabs Pczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola |
| 2018 | COLT | An Estimate Sequence for Geodesically Convex Optimization. | Hongyi Zhang, Suvrit Sra |
| 2018 | CVPR | Non-Linear Temporal Subspace Representations for Activity Recognition. | Anoop Cherian, Suvrit Sra, Stephen Gould, Richard Hartley |
| 2018 | ICLR | Distributional Adversarial Networks. | Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra |
| 2018 | ICLR | Global Optimality Conditions for Deep Neural Networks. | Chulhee Yun, Suvrit Sra, Ali Jadbabaie |
| 2017 | AISTATS | Combinatorial Topic Models using Small-Variance Asymptotics. | Ke Jiang, Suvrit Sra, Brian Kulis |
| 2016 | AISTATS | Efficient Sampling for k-Determinantal Point Processes. | Chengtao Li, Stefanie Jegelka, Suvrit Sra |
| 2016 | AISTATS | AdaDelay: Delay Adaptive Distributed Stochastic Optimization. | Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola |
| 2016 | COLT | First-order Methods for Geodesically Convex Optimization. | Hongyi Zhang, Suvrit Sra |
| 2016 | ICML | Fast DPP Sampling for Nystrom with Application to Kernel Methods. | Chengtao Li, Stefanie Jegelka, Suvrit Sra |
| 2016 | ICML | Gaussian quadrature for matrix inverse forms with applications. | Chengtao Li, Suvrit Sra, Stefanie Jegelka |
| 2016 | ICML | Stochastic Variance Reduction for Nonconvex Optimization. | Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabs Pczos, Alexander J. Smola |
| 2016 | ICML | Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. | Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing |
| 2016 | ICML | Geometric Mean Metric Learning. | Pourya Zadeh, Reshad Hosseini, Suvrit Sra |
| 2015 | AISTATS | Data modeling with the elliptical gamma distribution. | Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge |
| 2015 | ICML | Fixed-point algorithms for learning determinantal point processes. | Zelda Mariet, Suvrit Sra |
| 2015 | UAI | Large-scale randomized-coordinate descent methods with non-separable linear constraints. | Sashank J. Reddi, Ahmed Hefny, Carlton Downey, Avinava Dubey, Suvrit Sra |
| 2014 | ECCV | Riemannian Sparse Coding for Positive Definite Matrices. | Anoop Cherian, Suvrit Sra |
| 2014 | ICML | Towards an optimal stochastic alternating direction method of multipliers. | Samaneh Azadi, Suvrit Sra |
| 2014 | ICML | Randomized Nonlinear Component Analysis. | David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schlkopf |
| 2014 | UAI | Fast Newton methods for the group fused lasso. | Matt Wytock, Suvrit Sra, Jeremy Z. Kolter |
| 2011 | ICASSP | Denoising sparse noise via online dictionary learning. | Anoop Cherian, Suvrit Sra, Nikolaos Papanikolopoulos |
| 2011 | ICCV | Efficient similarity search for covariance matrices via the Jensen-Bregman LogDet Divergence. | Anoop Cherian, Suvrit Sra, Arindam Banerjee, Nikolaos Papanikolopoulos |
| 2011 | ICML | Fast Newton-type Methods for Total Variation Regularization. | lvaro Barbero Jimnez, Suvrit Sra |
| 2010 | CVPR | Efficient filter flow for space-variant multiframe blind deconvolution. | Michael Hirsch, Suvrit Sra, Bernhard Schlkopf, Stefan Harmeling |
| 2010 | ICIP | Multiframe blind deconvolution, super-resolution, and saturation correction via incremental EM. | Stefan Harmeling, Suvrit Sra, Michael Hirsch, Bernhard Schlkopf |
| 2010 | ICML | A scalable trust-region algorithm with application to mixed-norm regression. | Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon |
| 2009 | ALT | Approximation Algorithms for Tensor Clustering. | Stefanie Jegelka, Suvrit Sra, Arindam Banerjee |
| 2009 | ICML | Workshop summary: Numerical mathematics in machine learning. | Matthias W. Seeger, Suvrit Sra, John P. Cunningham |
| 2008 | ICDM | Block-Iterative Algorithms for Non-negative Matrix Approximation. | Suvrit Sra |
| 2007 | ICML | Information-theoretic metric learning. | Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon |
| 2007 | SDM | Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem. | Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon |
| 2006 | ICASSP | Row-Action Methods for Compressed Sensing. | Suvrit Sra, Joel A. Tropp |
| 2004 | SDM | Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data. | Hyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, Suvrit Sra |
| 2003 | KDD | Generative model-based clustering of directional data. | Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra |