← Authors

Suvrit Sra

80 papers · 2005–2025 · 10 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (19) 🌈 Renaissance Researcher (7) 🐝 Cross-Pollinator (12) πŸƒ Academic Marathon (20) 🌍 Conference Polyglot (10) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (4) 🏠 Conference Loyalist (28) 🧬 Topic Evolution 🀝 Dynamic Duo (14) πŸ† Grand Slam πŸ‘‘ Triple Crown 🌱 Topic Pioneer πŸ”¬ Deep Specialist (30) πŸ† Keyword Champion (2) ⚑ Prolific Year (6) πŸ—ƒοΈ Keyword Collector (89) πŸ“ˆ Trend Setter πŸ’Ž Century Club (80) πŸ”₯ Unstoppable (14) ❓ The Questioner (5) πŸš€ Conference Pioneer

Conferences

NIPS (28) ICML (24) ICLR (10) AISTATS (6) COLT (5) JMLR (2) L4DC (2) AAAI (1) ACML (1) CVPR (1)

Top co-authors

Ali Jadbabaie (14) Stefanie Jegelka (13) Jingzhao Zhang (10) Chulhee Yun (10) Kwangjun Ahn (7) Chengtao Li (5) Xiang Cheng (5) Reshad Hosseini (4) Sashank J. Reddi (4) Alp Yurtsever (4)

Keywords

determinantal point process (9) convex optimization (8) stochastic gradient descent (8) nonconvex optimization (8) stochastic optimization (7) manifold optimization (6) riemannian optimization (6) convergence rate (5) variance reduction (5) gradient descent (5) positive definite matrix (4) riemannian manifold (4) markov chain monte carlo (4) stochastic gradient (4) non-convex optimization (4) nonsmooth optimization (4) first-order method (4) neural network optimization (3) riemannian geometry (3) representation learning (3)

Papers

Graph Transformers Dream of Electric Flow ICLR 2025
Linear attention is (maybe) all you need (to understand Transformer optimization) ICLR 2024
Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context ICML 2024
First-Order Methods for Linearly Constrained Bilevel Optimization NIPS 2024
How to Escape Sharp Minima with Random Perturbations ICML 2024
Transformers learn to implement preconditioned gradient descent for in-context learning NIPS 2023
The Crucial Role of Normalization in Sharpness-Aware Minimization NIPS 2023
Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control? L4DC 2023
On the Training Instability of Shuffling SGD with Batch Normalization ICML 2023
Global optimality for Euclidean CCCP under Riemannian convexity ICML 2023
Sign and Basis Invariant Networks for Spectral Graph Representation Learning ICLR 2023
Understanding the unstable convergence of gradient descent ICML 2022
Efficient Sampling on Riemannian Manifolds via Langevin MCMC NIPS 2022
CCCP is Frank-Wolfe in disguise NIPS 2022
Max-Margin Contrastive Learning AAAI 2022
Understanding Riemannian Acceleration via a Proximal Extragradient Framework COLT 2022
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond ICLR 2022
Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective ICML 2022
Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity ICML 2022
Time Varying Regression with Hidden Linear Dynamics L4DC 2022
Three Operator Splitting with a Nonconvex Loss Function ICML 2021
Contrastive Learning with Hard Negative Samples ICLR 2021
Provably Efficient Algorithms for Multi-Objective Competitive RL ICML 2021
Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates NIPS 2021
Coping with Label Shift via Distributionally Robust Optimisation ICLR 2021
Open Problem: Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? COLT 2021
Can contrastive learning avoid shortcut solutions? NIPS 2021
Online Learning in Unknown Markov Games ICML 2021
Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity ICLR 2020
Why are Adaptive Methods Good for Attention Models? NIPS 2020
Geodesically-convex optimization for averaging partially observed covariance matrices ACML 2020
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions ICML 2020
Strength from Weakness: Fast Learning Using Weak Supervision ICML 2020
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes NIPS 2020
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition ICML 2020
From Nesterov’s Estimate Sequence to Riemannian Acceleration COLT 2020
SGD with shuffling: optimal rates without component convexity and large epoch requirements NIPS 2020
Flexible Modeling of Diversity with Strongly Log-Concave Distributions NIPS 2019
Small nonlinearities in activation functions create bad local minima in neural networks ICLR 2019
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity NIPS 2019
Are deep ResNets provably better than linear predictors? NIPS 2019
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator ICML 2019
Random Shuffling Beats SGD after Finite Epochs ICML 2019
Escaping Saddle Points with Adaptive Gradient Methods ICML 2019
Learning Determinantal Point Processes by Corrective Negative Sampling AISTATS 2019
Efficiently testing local optimality and escaping saddles for ReLU networks ICLR 2019
A Generic Approach for Escaping Saddle points AISTATS 2018
An Estimate Sequence for Geodesically Convex Optimization COLT 2018
Non-Linear Temporal Subspace Representations for Activity Recognition CVPR 2018
Global Optimality Conditions for Deep Neural Networks ICLR 2018
Modular Proximal Optimization for Multidimensional Total-Variation Regularization JMLR 2018
Exponentiated Strongly Rayleigh Distributions NIPS 2018
Direct Runge-Kutta Discretization Achieves Acceleration NIPS 2018
Combinatorial Topic Models using Small-Variance Asymptotics AISTATS 2017
Elementary Symmetric Polynomials for Optimal Experimental Design NIPS 2017
Polynomial time algorithms for dual volume sampling NIPS 2017
Gaussian quadrature for matrix inverse forms with applications ICML 2016
Fast DPP Sampling for Nystrom with Application to Kernel Methods ICML 2016
Geometric Mean Metric Learning ICML 2016
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling NIPS 2016
First-order Methods for Geodesically Convex Optimization COLT 2016
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization NIPS 2016
Stochastic Variance Reduction for Nonconvex Optimization ICML 2016
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms ICML 2016
Efficient Sampling for k-Determinantal Point Processes AISTATS 2016
AdaDelay: Delay Adaptive Distributed Stochastic Optimization AISTATS 2016
Kronecker Determinantal Point Processes NIPS 2016
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds NIPS 2016
Matrix Manifold Optimization for Gaussian Mixtures NIPS 2015
Fixed-point algorithms for learning determinantal point processes ICML 2015
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants NIPS 2015
Data modeling with the elliptical gamma distribution AISTATS 2015
Towards an optimal stochastic alternating direction method of multipliers ICML 2014
Efficient Structured Matrix Rank Minimization NIPS 2014
Randomized Nonlinear Component Analysis ICML 2014
Reflection methods for user-friendly submodular optimization NIPS 2013
Geometric optimisation on positive definite matrices for elliptically contoured distributions NIPS 2013
Scalable nonconvex inexact proximal splitting NIPS 2012
A new metric on the manifold of kernel matrices with application to matrix geometric means NIPS 2012
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions JMLR 2005