Suvrit Sra - RankMe

222 papers A* 62A 11B 2C 1Misc 1Journal 110Unranked 34

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part II. CoRR Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra
2025	J	`jnl`	A projection-based framework for gradient-free and parallel learning. CoRR Andreas Bergmeister, Manish Krishan Lal, Stefanie Jegelka, Suvrit Sra
2025	J	`jnl`	Cross-fluctuation phase transitions reveal sampling dynamics in diffusion models. CoRR Sai Niranjan Ramachandran, Manish Krishan Lal, Suvrit Sra
2025	A*	`conf`	Graph Transformers Dream of Electric Flow. ICLR Xiang Cheng, Lawrence Carin, Suvrit Sra
2025	J	`jnl`	Implicit Bias in Matrix Factorization and its Explicit Realization in a New Architecture. CoRR Yikun Hou, Suvrit Sra, Alp Yurtsever
2025	—	`conf`	Improved Rates for Stochastic Variance-Reduced Difference-of-Convex Algorithms. CDC Anh Duc Nguyen, Alp Yurtsever, Suvrit Sra, Kim-Chuan Toh
2025	J	`jnl`	Linearly Convergent Algorithms for Nonsmooth Problems with Unknown Smooth Pieces. CoRR Zhe Zhang, Suvrit Sra
2025	J	`jnl`	Randomized block coordinate DC algorithm. EURO J. Comput. Optim. Hoomaan Maskan, Paniz Halvachi, Suvrit Sra, Alp Yurtsever
2025	J	`jnl`	Revisiting Frank-Wolfe for Structured Nonconvex Optimization. CoRR Hoomaan Maskan, Yikun Hou, Suvrit Sra, Alp Yurtsever
2024	J	`jnl`	Efficient Sampling on Riemannian Manifolds via Langevin MCMC. CoRR Xiang Cheng, Jingzhao Zhang, Suvrit Sra
2024	A*	`conf`	First-Order Methods for Linearly Constrained Bilevel Optimization. NeurIPS Guy Kornowski, Swati Padmanabhan, Kai Wang, Zhe Zhang, Suvrit Sra
2024	J	`jnl`	First-Order Methods for Linearly Constrained Bilevel Optimization. CoRR Guy Kornowski, Swati Padmanabhan, Kai Wang, Zhe Zhang, Suvrit Sra
2024	J	`jnl`	Graph Transformers Dream of Electric Flow. CoRR Xiang Cheng, Lawrence Carin, Suvrit Sra
2024	A*	`conf`	How to Escape Sharp Minima with Random Perturbations. ICML Kwangjun Ahn, Ali Jadbabaie, Suvrit Sra
2024	A*	`conf`	Linear attention is (maybe) all you need (to understand Transformer optimization). ICLR Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra
2024	J	`jnl`	Memory-augmented Transformers can implement Linear First-Order Optimization Methods. CoRR Sanchayan Dutta, Suvrit Sra
2024	J	`jnl`	Riemannian Bilevel Optimization. CoRR Sanchayan Dutta, Xiang Cheng, Suvrit Sra
2024	A*	`conf`	Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context. ICML Xiang Cheng, Yuxin Chen, Suvrit Sra
2023	—	`conf`	Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control? L4DC Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra
2023	A*	`conf`	Global optimality for Euclidean CCCP under Riemannian convexity. ICML Melanie Weber, Suvrit Sra
2023	J	`jnl`	How to escape sharp minima. CoRR Kwangjun Ahn, Ali Jadbabaie, Suvrit Sra
2023	J	`jnl`	Invex Programs: First Order Algorithms and Their Convergence. CoRR Adarsh Barik, Suvrit Sra, Jean Honorio
2023	J	`jnl`	Linear attention is (maybe) all you need (to understand transformer optimization). CoRR Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra
2023	A*	`conf`	On the Training Instability of Shuffling SGD with Batch Normalization. ICML David Xing Wu, Chulhee Yun, Suvrit Sra
2023	J	`jnl`	On the Training Instability of Shuffling SGD with Batch Normalization. CoRR David Xing Wu, Chulhee Yun, Suvrit Sra
2023	J	`jnl`	Riemannian Optimization via Frank-Wolfe Methods. Math. Program. Melanie Weber, Suvrit Sra
2023	A*	`conf`	Sign and Basis Invariant Networks for Spectral Graph Representation Learning. ICLR Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka
2023	J	`jnl`	Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm. SIAM J. Optim. Peiyuan Zhang, Jingzhao Zhang, Suvrit Sra
2023	A*	`conf`	The Crucial Role of Normalization in Sharpness-Aware Minimization. NeurIPS Yan Dai, Kwangjun Ahn, Suvrit Sra
2023	J	`jnl`	The Crucial Role of Normalization in Sharpness-Aware Minimization. CoRR Yan Dai, Kwangjun Ahn, Suvrit Sra
2023	—	`conf`	Toward Understanding State Representation Learning in MuZero: A Case Study in Linear Quadratic Gaussian Control. CDC Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra
2023	J	`jnl`	Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context. CoRR Xiang Cheng, Yuxin Chen, Suvrit Sra
2023	A*	`conf`	Transformers learn to implement preconditioned gradient descent for in-context learning. NeurIPS Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, Suvrit Sra
2023	J	`jnl`	Transformers learn to implement preconditioned gradient descent for in-context learning. CoRR Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, Suvrit Sra
2022	A*	`conf`	Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity. ICML Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie
2022	A*	`conf`	CCCP is Frank-Wolfe in disguise. NeurIPS Alp Yurtsever, Suvrit Sra
2022	J	`jnl`	Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control? CoRR Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra
2022	J	`jnl`	Computing Brascamp-Lieb Constants through the lens of Thompson Geometry. CoRR Melanie Weber, Suvrit Sra
2022	A*	`conf`	Efficient Sampling on Riemannian Manifolds via Langevin MCMC. NeurIPS Xiang Cheng, Jingzhao Zhang, Suvrit Sra
2022	A*	`conf`	Max-Margin Contrastive Learning. AAAI Anshul Shah, Suvrit Sra, Rama Chellappa, Anoop Cherian
2022	A*	`conf`	Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. ICLR Chulhee Yun, Shashank Rajput, Suvrit Sra
2022	J	`jnl`	Minimax in Geodesic Metric Spaces: Sion's Theorem and Algorithms. CoRR Peiyuan Zhang, Jingzhao Zhang, Suvrit Sra
2022	A*	`conf`	Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective. ICML Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie
2022	J	`jnl`	On a class of geodesically convex optimization problems solved via Euclidean MM methods. CoRR Suvrit Sra, Melanie Weber
2022	J	`jnl`	Sign and Basis Invariant Networks for Spectral Graph Representation Learning. CoRR Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka
2022	—	`conf`	Time Varying Regression with Hidden Linear Dynamics. L4DC Horia Mania, Ali Jadbabaie, Devavrat Shah, Suvrit Sra
2022	—	`conf`	Understanding Nesterov's Acceleration via Proximal Point Method. SOSA Kwangjun Ahn, Suvrit Sra
2022	A*	`conf`	Understanding Riemannian Acceleration via a Proximal Extragradient Framework. COLT Jikai Jin, Suvrit Sra
2022	A*	`conf`	Understanding the unstable convergence of gradient descent. ICML Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra
2022	J	`jnl`	Understanding the unstable convergence of gradient descent. CoRR Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra
2021	J	`jnl`	A Riemannian Accelerated Proximal Extragradient Framework and its Implications. CoRR Jikai Jin, Suvrit Sra
2021	J	`jnl`	Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? CoRR Chulhee Yun, Suvrit Sra, Ali Jadbabaie
2021	A*	`conf`	Can contrastive learning avoid shortcut solutions? NeurIPS Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra
2021	J	`jnl`	Can contrastive learning avoid shortcut solutions? CoRR Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra
2021	A*	`conf`	Contrastive Learning with Hard Negative Samples. ICLR Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka
2021	A*	`conf`	Coping with Label Shift via Distributionally Robust Optimisation. ICLR Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra
2021	J	`jnl`	Max-Margin Contrastive Learning. CoRR Anshul Shah, Suvrit Sra, Rama Chellappa, Anoop Cherian
2021	J	`jnl`	Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. CoRR Chulhee Yun, Shashank Rajput, Suvrit Sra
2021	J	`jnl`	On Convergence of Training Loss Without Reaching Stationary Points. CoRR Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie
2021	A*	`conf`	Online Learning in Unknown Markov Games. ICML Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra
2021	A*	`conf`	Open Problem: Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? COLT Chulhee Yun, Suvrit Sra, Ali Jadbabaie
2021	A*	`conf`	Provably Efficient Algorithms for Multi-Objective Competitive RL. ICML Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra
2021	J	`jnl`	Provably Efficient Algorithms for Multi-Objective Competitive RL. CoRR Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra
2021	A*	`conf`	Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates. NeurIPS Alp Yurtsever, Alex Gu, Suvrit Sra
2021	A*	`conf`	Three Operator Splitting with a Nonconvex Loss Function. ICML Alp Yurtsever, Varun Mangalick, Suvrit Sra
2020	J	`jnl`	An Interpretable Predictive Model of Vaccine Utilization for Tanzania. Frontiers Artif. Intell. Ramkumar Hariharan, Johnna Sundberg, Giacomo Gallino, Ashley Schmidt, Drew Arenth, Suvrit Sra, Benjamin Fels
2020	J	`jnl`	An alternative to EM for Gaussian mixture models: batch and stochastic Riemannian optimization. Math. Program. Reshad Hosseini, Suvrit Sra
2020	A*	`conf`	Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. ICML Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie
2020	J	`jnl`	Contrastive Learning with Hard Negative Samples. CoRR Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka
2020	J	`jnl`	Coping with Label Shift via Distributionally Robust Optimisation. CoRR Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra
2020	A*	`conf`	From Nesterov's Estimate Sequence to Riemannian Acceleration. COLT Kwangjun Ahn, Suvrit Sra
2020	C	`conf`	Geodesically-convex optimization for averaging partially observed covariance matrices. ACML Florian Yger, Sylvain Chevallier, Quentin Barthélemy, Suvrit Sra
2020	A*	`conf`	Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition. ICML Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu
2020	J	`jnl`	On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions. CoRR Jingzhao Zhang, Hongzhou Lin, Suvrit Sra, Ali Jadbabaie
2020	J	`jnl`	On Tight Convergence Rates of Without-replacement SGD. CoRR Kwangjun Ahn, Suvrit Sra
2020	J	`jnl`	Provably Efficient Online Agnostic Learning in Markov Games. CoRR Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra
2020	A*	`conf`	SGD with shuffling: optimal rates without component convexity and large epoch requirements. NeurIPS Kwangjun Ahn, Chulhee Yun, Suvrit Sra
2020	J	`jnl`	Stochastic Optimization with Non-stationary Noise. CoRR Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie
2020	A*	`conf`	Strength from Weakness: Fast Learning Using Weak Supervision. ICML Joshua Robinson, Stefanie Jegelka, Suvrit Sra
2020	J	`jnl`	Strength from Weakness: Fast Learning Using Weak Supervision. CoRR Joshua Robinson, Stefanie Jegelka, Suvrit Sra
2020	A*	`conf`	Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes. NeurIPS Yi Tian, Jian Qian, Suvrit Sra
2020	J	`jnl`	Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes. CoRR Yi Tian, Jian Qian, Suvrit Sra
2020	A*	`conf`	Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity. ICLR Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie
2020	A*	`conf`	Why are Adaptive Methods Good for Attention Models? NeurIPS Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra
2020	J	`jnl`	Why do classifier accuracies show linear trends under distribution shift? CoRR Horia Mania, Suvrit Sra
2019	—	`conf`	Acceleration in First Order Quasi-strongly Convex Optimization by ODE Discretization. CDC Jingzhao Zhang, Suvrit Sra, Ali Jadbabaie
2019	J	`jnl`	Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition. CoRR Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie
2019	A*	`conf`	Are deep ResNets provably better than linear predictors? NeurIPS Chulhee Yun, Suvrit Sra, Ali Jadbabaie
2019	J	`jnl`	Are deep ResNets provably better than linear predictors? CoRR Chulhee Yun, Suvrit Sra, Ali Jadbabaie
2019	A*	`conf`	Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator. ICML Alp Yurtsever, Suvrit Sra, Volkan Cevher
2019	—	`conf`	Efficiently testing local optimality and escaping saddles for ReLU networks. ICLR (Poster) Chulhee Yun, Suvrit Sra, Ali Jadbabaie
2019	A*	`conf`	Escaping Saddle Points with Adaptive Gradient Methods. ICML Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra
2019	J	`jnl`	Escaping Saddle Points with Adaptive Gradient Methods. CoRR Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra
2019	A*	`conf`	Flexible Modeling of Diversity with Strongly Log-Concave Distributions. NeurIPS Joshua Robinson, Suvrit Sra, Stefanie Jegelka
2019	J	`jnl`	Flexible Modeling of Diversity with Strongly Log-Concave Distributions. CoRR Joshua Robinson, Suvrit Sra, Stefanie Jegelka
2019	A	`conf`	Learning Determinantal Point Processes by Corrective Negative Sampling. AISTATS Zelda Mariet, Mike Gartrell, Suvrit Sra
2019	J	`jnl`	Metrics Induced by Quantum Jensen-Shannon-Renyí and Related Divergences. CoRR Suvrit Sra
2019	J	`jnl`	Nonconvex stochastic optimization on manifolds via Riemannian Frank-Wolfe methods. CoRR Melanie Weber, Suvrit Sra
2019	A*	`conf`	Random Shuffling Beats SGD after Finite Epochs. ICML Jeff Z. HaoChen, Suvrit Sra
2019	A*	`conf`	Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity. NeurIPS Chulhee Yun, Suvrit Sra, Ali Jadbabaie
2019	—	`conf`	Small nonlinearities in activation functions create bad local minima in neural networks. ICLR (Poster) Chulhee Yun, Suvrit Sra, Ali Jadbabaie
2019	J	`jnl`	Why ADAM Beats SGD for Attention Models. CoRR Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra
2018	J	`jnl`	A Critical View of Global Optimality in Deep Learning. CoRR Chulhee Yun, Suvrit Sra, Ali Jadbabaie
2018	A	`conf`	A Generic Approach for Escaping Saddle points. AISTATS Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola
2018	A*	`conf`	An Estimate Sequence for Geodesically Convex Optimization. COLT Hongyi Zhang, Suvrit Sra
2018	J	`jnl`	Deep-RBF Networks Revisited: Robust Classification with Rejection. CoRR Pourya Habib Zadeh, Reshad Hosseini, Suvrit Sra
2018	A*	`conf`	Direct Runge-Kutta Discretization Achieves Acceleration. NeurIPS Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie
2018	J	`jnl`	Direct Runge-Kutta Discretization Achieves Acceleration. CoRR Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie
2018	—	`conf`	Distributional Adversarial Networks. ICLR (Workshop) Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra
2018	J	`jnl`	Efficiently testing local optimality and escaping saddles for ReLU networks. CoRR Chulhee Yun, Suvrit Sra, Ali Jadbabaie
2018	A*	`conf`	Exponentiated Strongly Rayleigh Distributions. NeurIPS Zelda E. Mariet, Suvrit Sra, Stefanie Jegelka
2018	J	`jnl`	Finite sample expressive power of small-width ReLU networks. CoRR Chulhee Yun, Suvrit Sra, Ali Jadbabaie
2018	—	`conf`	Global Optimality Conditions for Deep Neural Networks. ICLR (Poster) Chulhee Yun, Suvrit Sra, Ali Jadbabaie
2018	J	`jnl`	Learning Determinantal Point Processes by Sampling Inferred Negatives. CoRR Zelda Mariet, Mike Gartrell, Suvrit Sra
2018	J	`jnl`	Modular Proximal Optimization for Multidimensional Total-Variation Regularization. J. Mach. Learn. Res. Álvaro Barbero Jiménez, Suvrit Sra
2018	A*	`conf`	Non-Linear Temporal Subspace Representations for Activity Recognition. CVPR Anoop Cherian, Suvrit Sra, Stephen Gould, Richard Hartley
2018	J	`jnl`	Non-Linear Temporal Subspace Representations for Activity Recognition. CoRR Anoop Cherian, Suvrit Sra, Stephen Gould, Richard Hartley
2018	—	`conf`	On Geodesically Convex Formulations for the Brascamp-Lieb Constant. APPROX-RANDOM Suvrit Sra, Nisheeth K. Vishnoi, Ozan Yildiz
2018	J	`jnl`	R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate. CoRR Jingzhao Zhang, Hongyi Zhang, Suvrit Sra
2018	J	`jnl`	Towards Riemannian Accelerated Gradient Methods. CoRR Hongyi Zhang, Suvrit Sra
2017	J	`jnl`	A Generic Approach for Escaping Saddle points. CoRR Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola
2017	J	`jnl`	An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization. CoRR Reshad Hosseini, Suvrit Sra
2017	A	`conf`	Combinatorial Topic Models using Small-Variance Asymptotics. AISTATS Ke Jiang, Suvrit Sra, Brian Kulis
2017	J	`jnl`	Distributional Adversarial Networks. CoRR Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra
2017	—	`conf`	Elementary Symmetric Polynomials for Optimal Experimental Design. NIPS Zelda E. Mariet, Suvrit Sra
2017	J	`jnl`	Frank-Wolfe methods for geodesically convex optimization with application to the matrix geometric mean. CoRR Melanie Weber, Suvrit Sra
2017	J	`jnl`	Global optimality conditions for deep neural networks. CoRR Chulhee Yun, Suvrit Sra, Ali Jadbabaie
2017	—	`conf`	Polynomial time algorithms for dual volume sampling. NIPS Chengtao Li, Stefanie Jegelka, Suvrit Sra
2017	J	`jnl`	Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices. IEEE Trans. Neural Networks Learn. Syst. Anoop Cherian, Suvrit Sra
2017	J	`jnl`	Sequence Summarization Using Order-constrained Kernelized Feature Subspaces. CoRR Anoop Cherian, Suvrit Sra, Richard Hartley
2017	J	`jnl`	Unsupervised robust nonparametric learning of hidden community properties. CoRR Mikhail A. Langovoy, Akhilesh Gotmare, Martin Jaggi, Suvrit Sra
2016	A	`conf`	AdaDelay: Delay Adaptive Distributed Stochastic Optimization. AISTATS Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola
2016	J	`jnl`	Combinatorial Topic Models using Small-Variance Asymptotics. CoRR Ke Jiang, Suvrit Sra, Brian Kulis
2016	A*	`conf`	Diversity Networks. ICLR (Poster) Zelda Mariet, Suvrit Sra
2016	A	`conf`	Efficient Sampling for k-Determinantal Point Processes. AISTATS Chengtao Li, Stefanie Jegelka, Suvrit Sra
2016	J	`jnl`	Entropic metric alignment for correspondence problems. ACM Trans. Graph. Justin Solomon, Gabriel Peyré, Vladimir G. Kim, Suvrit Sra
2016	A*	`conf`	Fast DPP Sampling for Nystrom with Application to Kernel Methods. ICML Chengtao Li, Stefanie Jegelka, Suvrit Sra
2016	J	`jnl`	Fast DPP Sampling for Nyström with Application to Kernel Methods. CoRR Chengtao Li, Stefanie Jegelka, Suvrit Sra
2016	J	`jnl`	Fast Incremental Method for Nonconvex Optimization. CoRR Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	—	`conf`	Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling. NIPS Chengtao Li, Suvrit Sra, Stefanie Jegelka
2016	J	`jnl`	Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes. CoRR Chengtao Li, Stefanie Jegelka, Suvrit Sra
2016	J	`jnl`	Fast Stochastic Methods for Nonsmooth Nonconvex Optimization. CoRR Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	—	`conf`	Fast incremental method for smooth nonconvex optimization. CDC Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	J	`jnl`	Fast stochastic optimization on Riemannian manifolds. CoRR Hongyi Zhang, Sashank J. Reddi, Suvrit Sra
2016	A*	`conf`	First-order Methods for Geodesically Convex Optimization. COLT Hongyi Zhang, Suvrit Sra
2016	J	`jnl`	First-order Methods for Geodesically Convex Optimization. CoRR Hongyi Zhang, Suvrit Sra
2016	A*	`conf`	Gaussian quadrature for matrix inverse forms with applications. ICML Chengtao Li, Suvrit Sra, Stefanie Jegelka
2016	A*	`conf`	Geometric Mean Metric Learning. ICML Pourya Zadeh, Reshad Hosseini, Suvrit Sra
2016	J	`jnl`	Geometric Mean Metric Learning. CoRR Pourya Zadeh, Reshad Hosseini, Suvrit Sra
2016	J	`jnl`	Inference and mixture modeling with the Elliptical Gamma Distribution. Comput. Stat. Data Anal. Reshad Hosseini, Suvrit Sra, Lucas Theis, Matthias Bethge
2016	—	`conf`	Kronecker Determinantal Point Processes. NIPS Zelda E. Mariet, Suvrit Sra
2016	J	`jnl`	Kronecker Determinantal Point Processes. CoRR Zelda Mariet, Suvrit Sra
2016	J	`jnl`	On inequalities for normalized Schur functions. Eur. J. Comb. Suvrit Sra
2016	A*	`conf`	Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. ICML Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing
2016	—	`conf`	Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization. NIPS Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	—	`conf`	Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds. NIPS Hongyi Zhang, Sashank J. Reddi, Suvrit Sra
2016	J	`jnl`	Stochastic Frank-Wolfe Methods for Nonconvex Optimization. CoRR Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	—	`conf`	Stochastic Frank-Wolfe methods for nonconvex optimization. Allerton Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	A*	`conf`	Stochastic Variance Reduction for Nonconvex Optimization. ICML Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2016	J	`jnl`	Stochastic Variance Reduction for Nonconvex Optimization. CoRR Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2015	J	`jnl`	AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization. CoRR Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola
2015	J	`jnl`	Bounds on bilinear inverse forms via Gaussian quadrature with applications. CoRR Chengtao Li, Suvrit Sra, Stefanie Jegelka
2015	J	`jnl`	Conic Geometric Optimization on the Manifold of Positive Definite Matrices. SIAM J. Optim. Suvrit Sra, Reshad Hosseini
2015	J	`jnl`	Convex Optimization for Parallel Energy Minimization. CoRR K. S. Sesh Kumar, Álvaro Barbero Jiménez, Stefanie Jegelka, Suvrit Sra, Francis R. Bach
2015	A	`conf`	Data modeling with the elliptical gamma distribution. AISTATS Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge
2015	J	`jnl`	Efficient Sampling for k-Determinantal Point Processes. CoRR Chengtao Li, Stefanie Jegelka, Suvrit Sra
2015	J	`jnl`	Efficient Structured Matrix Rank Minimization. CoRR Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra
2015	J	`jnl`	Fixed-point algorithms for determinantal point processes. CoRR Zelda Mariet, Suvrit Sra
2015	A*	`conf`	Fixed-point algorithms for learning determinantal point processes. ICML Zelda Mariet, Suvrit Sra
2015	A	`conf`	Large-scale randomized-coordinate descent methods with non-separable linear constraints. UAI Sashank J. Reddi, Ahmed Hefny, Carlton Downey, Avinava Dubey, Suvrit Sra
2015	J	`jnl`	Manifold Optimization for Gaussian Mixture Models. CoRR Reshad Hosseini, Suvrit Sra
2015	—	`conf`	Matrix Manifold Optimization for Gaussian Mixtures. NIPS Reshad Hosseini, Suvrit Sra
2015	—	`conf`	On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. NIPS Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2015	J	`jnl`	On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. CoRR Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola
2015	J	`jnl`	Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices. CoRR Anoop Cherian, Suvrit Sra
2014	J	`jnl`	Efficient Nearest Neighbors via Robust Sparse Hashing. IEEE Trans. Image Process. Anoop Cherian, Suvrit Sra, Vassilios Morellas, Nikolaos Papanikolopoulos
2014	—	`conf`	Efficient Structured Matrix Rank Minimization. NIPS Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra
2014	A	`conf`	Fast Newton methods for the group fused lasso. UAI Matt Wytock, Suvrit Sra, Jeremy Z. Kolter
2014	A*	`conf`	Randomized Nonlinear Component Analysis. ICML David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf
2014	J	`jnl`	Randomized Nonlinear Component Analysis. CoRR David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf
2014	—	`conf`	Riemannian Sparse Coding for Positive Definite Matrices. ECCV (3) Anoop Cherian, Suvrit Sra
2014	A*	`conf`	Towards an optimal stochastic alternating direction method of multipliers. ICML Samaneh Azadi, Suvrit Sra
2014	—	`ch.`	Tractable Optimization in Machine Learning. Tractability Suvrit Sra
2013	J	`jnl`	A non-monotonic method for large-scale non-negative least squares. Optim. Methods Softw. Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
2013	—	`conf`	Geometric optimisation on positive definite matrices for elliptically contoured distributions. NIPS Suvrit Sra, Reshad Hosseini
2013	J	`jnl`	Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices. IEEE Trans. Pattern Anal. Mach. Intell. Anoop Cherian, Suvrit Sra, Arindam Banerjee, Nikolaos Papanikolopoulos
2013	—	`conf`	Reflection methods for user-friendly submodular optimization. NIPS Stefanie Jegelka, Francis R. Bach, Suvrit Sra
2013	J	`jnl`	Reflection methods for user-friendly submodular optimization. CoRR Stefanie Jegelka, Francis R. Bach, Suvrit Sra
2013	J	`jnl`	Statistical estimation for optimization problems on graphs. CoRR Mikhail A. Langovoy, Suvrit Sra
2013	J	`jnl`	The multivariate Watson distribution: Maximum-likelihood estimation and other aspects. J. Multivar. Anal. Suvrit Sra, Dmitrii Karp
2012	—	`conf`	A new metric on the manifold of kernel matrices with application to matrix geometric means. NIPS Suvrit Sra
2012	J	`jnl`	A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of Comput. Stat. Suvrit Sra
2012	J	`jnl`	Fast projections onto mixed-norm balls with applications CoRR Suvrit Sra
2012	J	`jnl`	Fast projections onto mixed-norm balls with applications. Data Min. Knowl. Discov. Suvrit Sra
2012	—	`conf`	Scalable nonconvex inexact proximal splitting. NIPS Suvrit Sra
2011	Misc	`conf`	Denoising sparse noise via online dictionary learning. ICASSP Anoop Cherian, Suvrit Sra, Nikolaos Papanikolopoulos
2011	A*	`conf`	Efficient similarity search for covariance matrices via the Jensen-Bregman LogDet Divergence. ICCV Anoop Cherian, Suvrit Sra, Arindam Banerjee, Nikolaos Papanikolopoulos
2011	A*	`conf`	Fast Newton-type Methods for Total Variation Regularization. ICML Álvaro Barbero Jiménez, Suvrit Sra
2011	—	`conf`	Fast Projections onto ℓ1, q -Norm Balls for Grouped Feature Selection. ECML/PKDD (3) Suvrit Sra
2011	—	`conf`	Generalized Dictionary Learning for Symmetric Positive Definite Matrices with Application to Nearest Neighbor Retrieval. ECML/PKDD (3) Suvrit Sra, Anoop Cherian
2010	A*	`conf`	A scalable trust-region algorithm with application to mixed-norm regression. ICML Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
2010	A*	`conf`	Efficient filter flow for space-variant multiframe blind deconvolution. CVPR Michael Hirsch, Suvrit Sra, Bernhard Schölkopf, Stefan Harmeling
2010	B	`conf`	Multiframe blind deconvolution, super-resolution, and saturation correction via incremental EM. ICIP Stefan Harmeling, Suvrit Sra, Michael Hirsch, Bernhard Schölkopf
2010	J	`jnl`	Tackling Box-Constrained Optimization via a New Projected Quasi-Newton Approach. SIAM J. Sci. Comput. Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
2009	J	`jnl`	A Trivial Observation related to Sparse Recovery CoRR Suvrit Sra
2009	B	`conf`	Approximation Algorithms for Tensor Clustering. ALT Stefanie Jegelka, Suvrit Sra, Arindam Banerjee
2009	A	`conf`	Convex Perturbations for Scalable Semidefinite Programming. AISTATS Brian Kulis, Suvrit Sra, Inderjit S. Dhillon
2009	A*	`conf`	Workshop summary: Numerical mathematics in machine learning. ICML Matthias W. Seeger, Suvrit Sra, John P. Cunningham
2008	J	`jnl`	Approximation Algorithms for Bregman Co-clustering and Tensor Clustering CoRR Stefanie Jegelka, Suvrit Sra, Arindam Banerjee
2008	A*	`conf`	Block-Iterative Algorithms for Non-negative Matrix Approximation. ICDM Suvrit Sra
2008	J	`jnl`	Fast Projection-Based Methods for the Least Squares Nonnegative Matrix Approximation Problem. Stat. Anal. Data Min. Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
2008	J	`jnl`	The Metric Nearness Problem. SIAM J. Matrix Anal. Appl. Justin Brickell, Inderjit S. Dhillon, Suvrit Sra, Joel A. Tropp
2007	A	`conf`	Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem. SDM Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
2007	A*	`conf`	Information-theoretic metric learning. ICML Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon
2006	—	`conf`	Efficient Large Scale Linear Programming Support Vector Machines. ECML Suvrit Sra
2006	—	`conf`	Incremental Aspect Models for Mining Document Streams. PKDD Arun C. Surendran, Suvrit Sra
2006	—	`conf`	Row-Action Methods for Compressed Sensing. ICASSP (3) Suvrit Sra, Joel A. Tropp
2005	J	`jnl`	Clustering on the Unit Hypersphere using von Mises-Fisher Distributions. J. Mach. Learn. Res. Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra
2005	—	`conf`	Generalized Nonnegative Matrix Approximations with Bregman Divergences. NIPS Inderjit S. Dhillon, Suvrit Sra
2004	A	`conf`	Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data. SDM Hyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, Suvrit Sra
2004	—	`conf`	Triangle Fixing Algorithms for the Metric Nearness Problem. NIPS Inderjit S. Dhillon, Suvrit Sra, Joel A. Tropp
2003	A*	`conf`	Generative model-based clustering of directional data. KDD Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra