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ORCIDSuvrit Sra
Person information
- affiliation: Massachusetts Institute of Technology (MIT), Laboratory for Information and Decision Systems, Cambridge, MA, USA
- affiliation: Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- affiliation: University of Texas at Austin, Department of Computer Sciences, Austin, TX, USA
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2020 – today
- 2024
[c106]Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra:
Linear attention is (maybe) all you need (to understand Transformer optimization). ICLR 2024- [i81]Xiang Cheng, Jingzhao Zhang, Suvrit Sra:
Efficient Sampling on Riemannian Manifolds via Langevin MCMC. CoRR abs/2402.10357 (2024) - [i80]Sanchayan Dutta, Xiang Cheng, Suvrit Sra:
Riemannian Bilevel Optimization. CoRR abs/2405.15816 (2024) - [i79]Guy Kornowski, Swati Padmanabhan, Kai Wang, Zhe Zhang, Suvrit Sra:
First-Order Methods for Linearly Constrained Bilevel Optimization. CoRR abs/2406.12771 (2024) - 2023
- [j20]Melanie Weber
, Suvrit Sra:
Riemannian Optimization via Frank-Wolfe Methods. Math. Program. 199(1): 525-556 (2023) - [j19]Peiyuan Zhang, Jingzhao Zhang, Suvrit Sra:
Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm. SIAM J. Optim. 33(4): 2885-2908 (2023) - [c105]Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra:
Toward Understanding State Representation Learning in MuZero: A Case Study in Linear Quadratic Gaussian Control. CDC 2023: 6166-6171 - [c104]Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka:
Sign and Basis Invariant Networks for Spectral Graph Representation Learning. ICLR 2023 - [c103]Melanie Weber, Suvrit Sra:
Global optimality for Euclidean CCCP under Riemannian convexity. ICML 2023: 36790-36803 - [c102]David Xing Wu, Chulhee Yun, Suvrit Sra:
On the Training Instability of Shuffling SGD with Batch Normalization. ICML 2023: 37787-37845 - [c101]Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra:
Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control? L4DC 2023: 51-63 - [c100]Yan Dai, Kwangjun Ahn, Suvrit Sra:
The Crucial Role of Normalization in Sharpness-Aware Minimization. NeurIPS 2023 - [c99]Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, Suvrit Sra:
Transformers learn to implement preconditioned gradient descent for in-context learning. NeurIPS 2023 - [i78]David Xing Wu, Chulhee Yun, Suvrit Sra:
On the Training Instability of Shuffling SGD with Batch Normalization. CoRR abs/2302.12444 (2023) - [i77]Yan Dai, Kwangjun Ahn, Suvrit Sra:
The Crucial Role of Normalization in Sharpness-Aware Minimization. CoRR abs/2305.15287 (2023) - [i76]Kwangjun Ahn, Ali Jadbabaie, Suvrit Sra:
How to escape sharp minima. CoRR abs/2305.15659 (2023) - [i75]Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, Suvrit Sra:
Transformers learn to implement preconditioned gradient descent for in-context learning. CoRR abs/2306.00297 (2023) - [i74]Adarsh Barik, Suvrit Sra, Jean Honorio:
Invex Programs: First Order Algorithms and Their Convergence. CoRR abs/2307.04456 (2023) - [i73]Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra:
Linear attention is (maybe) all you need (to understand transformer optimization). CoRR abs/2310.01082 (2023) - [i72]Xiang Cheng, Yuxin Chen, Suvrit Sra:
Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context. CoRR abs/2312.06528 (2023) - 2022
- [c98]Anshul Shah, Suvrit Sra, Rama Chellappa, Anoop Cherian:
Max-Margin Contrastive Learning. AAAI 2022: 8220-8230 - [c97]Jikai Jin, Suvrit Sra:
Understanding Riemannian Acceleration via a Proximal Extragradient Framework. COLT 2022: 2924-2962 - [c96]Chulhee Yun, Shashank Rajput, Suvrit Sra:
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. ICLR 2022 - [c95]Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra:
Understanding the unstable convergence of gradient descent. ICML 2022: 247-257 - [c94]Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie:
Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective. ICML 2022: 26330-26346 - [c93]Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie:
Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity. ICML 2022: 26347-26361 - [c92]Horia Mania, Ali Jadbabaie, Devavrat Shah, Suvrit Sra:
Time Varying Regression with Hidden Linear Dynamics. L4DC 2022: 858-869 - [c91]Xiang Cheng, Jingzhao Zhang, Suvrit Sra:
Efficient Sampling on Riemannian Manifolds via Langevin MCMC. NeurIPS 2022 - [c90]Alp Yurtsever, Suvrit Sra:
CCCP is Frank-Wolfe in disguise. NeurIPS 2022 - [c89]Kwangjun Ahn, Suvrit Sra:
Understanding Nesterov's Acceleration via Proximal Point Method. SOSA 2022: 117-130 - [i71]Peiyuan Zhang, Jingzhao Zhang, Suvrit Sra:
Minimax in Geodesic Metric Spaces: Sion's Theorem and Algorithms. CoRR abs/2202.06950 (2022) - [i70]Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka:
Sign and Basis Invariant Networks for Spectral Graph Representation Learning. CoRR abs/2202.13013 (2022) - [i69]Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra:
Understanding the unstable convergence of gradient descent. CoRR abs/2204.01050 (2022) - [i68]Suvrit Sra, Melanie Weber:
On a class of geodesically convex optimization problems solved via Euclidean MM methods. CoRR abs/2206.11426 (2022) - [i67]Melanie Weber, Suvrit Sra:
Computing Brascamp-Lieb Constants through the lens of Thompson Geometry. CoRR abs/2208.05013 (2022) - [i66]Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra:
Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control? CoRR abs/2212.14511 (2022) - 2021
- [c88]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Open Problem: Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? COLT 2021: 4653-4658 - [c87]Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka:
Contrastive Learning with Hard Negative Samples. ICLR 2021 - [c86]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. ICLR 2021 - [c85]Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra:
Online Learning in Unknown Markov Games. ICML 2021: 10279-10288 - [c84]Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra:
Provably Efficient Algorithms for Multi-Objective Competitive RL. ICML 2021: 12167-12176 - [c83]Alp Yurtsever, Varun Mangalick, Suvrit Sra:
Three Operator Splitting with a Nonconvex Loss Function. ICML 2021: 12267-12277 - [c82]Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra:
Can contrastive learning avoid shortcut solutions? NeurIPS 2021: 4974-4986 - [c81]Alp Yurtsever, Alex Gu, Suvrit Sra:
Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates. NeurIPS 2021: 19743-19756 - [i65]Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra:
Provably Efficient Algorithms for Multi-Objective Competitive RL. CoRR abs/2102.03192 (2021) - [i64]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Can Single-Shuffle SGD be Better than Reshuffling SGD and GD? CoRR abs/2103.07079 (2021) - [i63]Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra:
Can contrastive learning avoid shortcut solutions? CoRR abs/2106.11230 (2021) - [i62]Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie:
On Convergence of Training Loss Without Reaching Stationary Points. CoRR abs/2110.06256 (2021) - [i61]Chulhee Yun, Shashank Rajput, Suvrit Sra:
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. CoRR abs/2110.10342 (2021) - [i60]Jikai Jin, Suvrit Sra:
A Riemannian Accelerated Proximal Extragradient Framework and its Implications. CoRR abs/2111.02763 (2021) - [i59]Anshul Shah, Suvrit Sra, Rama Chellappa, Anoop Cherian:
Max-Margin Contrastive Learning. CoRR abs/2112.11450 (2021) - 2020
- [j18]Ramkumar Hariharan, Johnna Sundberg, Giacomo Gallino, Ashley Schmidt, Drew Arenth, Suvrit Sra, Benjamin Fels:
An Interpretable Predictive Model of Vaccine Utilization for Tanzania. Frontiers Artif. Intell. 3: 559617 (2020) - [j17]Reshad Hosseini, Suvrit Sra:
An alternative to EM for Gaussian mixture models: batch and stochastic Riemannian optimization. Math. Program. 181(1): 187-223 (2020) - [c80]Florian Yger, Sylvain Chevallier, Quentin Barthélemy, Suvrit Sra:
Geodesically-convex optimization for averaging partially observed covariance matrices. ACML 2020: 417-432 - [c79]Kwangjun Ahn, Suvrit Sra:
From Nesterov's Estimate Sequence to Riemannian Acceleration. COLT 2020: 84-118 - [c78]Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie:
Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity. ICLR 2020 - [c77]Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu:
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition. ICML 2020: 4860-4869 - [c76]Joshua Robinson, Stefanie Jegelka, Suvrit Sra:
Strength from Weakness: Fast Learning Using Weak Supervision. ICML 2020: 8127-8136 - [c75]Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie:
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. ICML 2020: 11173-11182 - [c74]Kwangjun Ahn, Chulhee Yun, Suvrit Sra:
SGD with shuffling: optimal rates without component convexity and large epoch requirements. NeurIPS 2020 - [c73]Yi Tian, Jian Qian, Suvrit Sra:
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes. NeurIPS 2020 - [c72]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why are Adaptive Methods Good for Attention Models? NeurIPS 2020 - [i58]Jingzhao Zhang, Hongzhou Lin, Suvrit Sra, Ali Jadbabaie:
On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions. CoRR abs/2002.04130 (2020) - [i57]Joshua Robinson, Stefanie Jegelka, Suvrit Sra:
Strength from Weakness: Fast Learning Using Weak Supervision. CoRR abs/2002.08483 (2020) - [i56]Kwangjun Ahn, Suvrit Sra:
On Tight Convergence Rates of Without-replacement SGD. CoRR abs/2004.08657 (2020) - [i55]Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie:
Stochastic Optimization with Non-stationary Noise. CoRR abs/2006.04429 (2020) - [i54]Yi Tian, Jian Qian, Suvrit Sra:
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes. CoRR abs/2006.13405 (2020) - [i53]Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka:
Contrastive Learning with Hard Negative Samples. CoRR abs/2010.04592 (2020) - [i52]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. CoRR abs/2010.12230 (2020) - [i51]Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra:
Provably Efficient Online Agnostic Learning in Markov Games. CoRR abs/2010.15020 (2020) - [i50]Horia Mania, Suvrit Sra:
Why do classifier accuracies show linear trends under distribution shift? CoRR abs/2012.15483 (2020)
2010 – 2019
- 2019
- [c71]Zelda Mariet, Mike Gartrell, Suvrit Sra:
Learning Determinantal Point Processes by Corrective Negative Sampling. AISTATS 2019: 2251-2260 - [c70]Jingzhao Zhang, Suvrit Sra, Ali Jadbabaie:
Acceleration in First Order Quasi-strongly Convex Optimization by ODE Discretization. CDC 2019: 1501-1506 - [c69]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Efficiently testing local optimality and escaping saddles for ReLU networks. ICLR (Poster) 2019 - [c68]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Small nonlinearities in activation functions create bad local minima in neural networks. ICLR (Poster) 2019 - [c67]Jeff Z. HaoChen, Suvrit Sra:
Random Shuffling Beats SGD after Finite Epochs. ICML 2019: 2624-2633 - [c66]Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra:
Escaping Saddle Points with Adaptive Gradient Methods. ICML 2019: 5956-5965 - [c65]Alp Yurtsever, Suvrit Sra, Volkan Cevher:
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator. ICML 2019: 7282-7291 - [c64]Joshua Robinson, Suvrit Sra, Stefanie Jegelka:
Flexible Modeling of Diversity with Strongly Log-Concave Distributions. NeurIPS 2019: 15199-15209 - [c63]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity. NeurIPS 2019: 15532-15543 - [c62]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Are deep ResNets provably better than linear predictors? NeurIPS 2019: 15660-15669 - [i49]Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra:
Escaping Saddle Points with Adaptive Gradient Methods. CoRR abs/1901.09149 (2019) - [i48]Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie:
Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition. CoRR abs/1905.11881 (2019) - [i47]Joshua Robinson, Suvrit Sra, Stefanie Jegelka:
Flexible Modeling of Diversity with Strongly Log-Concave Distributions. CoRR abs/1906.05413 (2019) - [i46]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Are deep ResNets provably better than linear predictors? CoRR abs/1907.03922 (2019) - [i45]Melanie Weber, Suvrit Sra:
Nonconvex stochastic optimization on manifolds via Riemannian Frank-Wolfe methods. CoRR abs/1910.04194 (2019) - [i44]Suvrit Sra:
Metrics Induced by Quantum Jensen-Shannon-Renyí and Related Divergences. CoRR abs/1911.02643 (2019) - [i43]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why ADAM Beats SGD for Attention Models. CoRR abs/1912.03194 (2019) - 2018
- [j16]Álvaro Barbero Jiménez, Suvrit Sra:
Modular Proximal Optimization for Multidimensional Total-Variation Regularization. J. Mach. Learn. Res. 19: 56:1-56:82 (2018) - [c61]Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola:
A Generic Approach for Escaping Saddle points. AISTATS 2018: 1233-1242 - [c60]Suvrit Sra, Nisheeth K. Vishnoi, Ozan Yildiz:
On Geodesically Convex Formulations for the Brascamp-Lieb Constant. APPROX-RANDOM 2018: 25:1-25:15 - [c59]Hongyi Zhang, Suvrit Sra:
An Estimate Sequence for Geodesically Convex Optimization. COLT 2018: 1703-1723 - [c58]Anoop Cherian, Suvrit Sra, Stephen Gould, Richard Hartley:
Non-Linear Temporal Subspace Representations for Activity Recognition. CVPR 2018: 2197-2206 - [c57]Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra:
Distributional Adversarial Networks. ICLR (Workshop) 2018 - [c56]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Global Optimality Conditions for Deep Neural Networks. ICLR (Poster) 2018 - [c55]Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie:
Direct Runge-Kutta Discretization Achieves Acceleration. NeurIPS 2018: 3904-3913 - [c54]Zelda E. Mariet, Suvrit Sra, Stefanie Jegelka:
Exponentiated Strongly Rayleigh Distributions. NeurIPS 2018: 4464-4474 - [i42]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
A Critical View of Global Optimality in Deep Learning. CoRR abs/1802.03487 (2018) - [i41]Zelda Mariet, Mike Gartrell, Suvrit Sra:
Learning Determinantal Point Processes by Sampling Inferred Negatives. CoRR abs/1802.05649 (2018) - [i40]Anoop Cherian, Suvrit Sra, Stephen Gould, Richard Hartley:
Non-Linear Temporal Subspace Representations for Activity Recognition. CoRR abs/1803.11064 (2018) - [i39]Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie:
Direct Runge-Kutta Discretization Achieves Acceleration. CoRR abs/1805.00521 (2018) - [i38]Hongyi Zhang, Suvrit Sra:
Towards Riemannian Accelerated Gradient Methods. CoRR abs/1806.02812 (2018) - [i37]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Efficiently testing local optimality and escaping saddles for ReLU networks. CoRR abs/1809.10858 (2018) - [i36]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Finite sample expressive power of small-width ReLU networks. CoRR abs/1810.07770 (2018) - [i35]Jingzhao Zhang, Hongyi Zhang, Suvrit Sra:
R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate. CoRR abs/1811.04194 (2018) - [i34]Pourya Habib Zadeh, Reshad Hosseini, Suvrit Sra:
Deep-RBF Networks Revisited: Robust Classification with Rejection. CoRR abs/1812.03190 (2018) - 2017
- [j15]Anoop Cherian, Suvrit Sra:
Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices. IEEE Trans. Neural Networks Learn. Syst. 28(12): 2859-2871 (2017) - [c53]Ke Jiang, Suvrit Sra, Brian Kulis:
Combinatorial Topic Models using Small-Variance Asymptotics. AISTATS 2017: 421-429 - [c52]Zelda E. Mariet, Suvrit Sra:
Elementary Symmetric Polynomials for Optimal Experimental Design. NIPS 2017: 2139-2148 - [c51]Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Polynomial time algorithms for dual volume sampling. NIPS 2017: 5038-5047 - [i33]Anoop Cherian, Suvrit Sra, Richard Hartley:
Sequence Summarization Using Order-constrained Kernelized Feature Subspaces. CoRR abs/1705.08583 (2017) - [i32]Reshad Hosseini, Suvrit Sra:
An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization. CoRR abs/1706.03267 (2017) - [i31]Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra:
Distributional Adversarial Networks. CoRR abs/1706.09549 (2017) - [i30]Chulhee Yun, Suvrit Sra, Ali Jadbabaie:
Global optimality conditions for deep neural networks. CoRR abs/1707.02444 (2017) - [i29]Mikhail A. Langovoy, Akhilesh Gotmare, Martin Jaggi, Suvrit Sra:
Unsupervised robust nonparametric learning of hidden community properties. CoRR abs/1707.03494 (2017) - [i28]Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola:
A Generic Approach for Escaping Saddle points. CoRR abs/1709.01434 (2017) - [i27]Melanie Weber, Suvrit Sra:
Frank-Wolfe methods for geodesically convex optimization with application to the matrix geometric mean. CoRR abs/1710.10770 (2017) - 2016
- [j14]Reshad Hosseini, Suvrit Sra, Lucas Theis, Matthias Bethge:
Inference and mixture modeling with the Elliptical Gamma Distribution. Comput. Stat. Data Anal. 101: 29-43 (2016) - [j13]Suvrit Sra:
On inequalities for normalized Schur functions. Eur. J. Comb. 51: 492-494 (2016) - [j12]Justin Solomon, Gabriel Peyré, Vladimir G. Kim, Suvrit Sra:
Entropic metric alignment for correspondence problems. ACM Trans. Graph. 35(4): 72:1-72:13 (2016) - [c50]Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola:
AdaDelay: Delay Adaptive Distributed Stochastic Optimization. AISTATS 2016: 957-965 - [c49]Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Efficient Sampling for k-Determinantal Point Processes. AISTATS 2016: 1328-1337 - [c48]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Frank-Wolfe methods for nonconvex optimization. Allerton 2016: 1244-1251 - [c47]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Fast incremental method for smooth nonconvex optimization. CDC 2016: 1971-1977 - [c46]Hongyi Zhang, Suvrit Sra:
First-order Methods for Geodesically Convex Optimization. COLT 2016: 1617-1638 - [c45]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Variance Reduction for Nonconvex Optimization. ICML 2016: 314-323 - [c44]Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing:
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. ICML 2016: 1548-1557 - [c43]Chengtao Li, Suvrit Sra, Stefanie Jegelka:
Gaussian quadrature for matrix inverse forms with applications. ICML 2016: 1766-1775 - [c42]Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Fast DPP Sampling for Nystrom with Application to Kernel Methods. ICML 2016: 2061-2070 - [c41]Pourya Zadeh, Reshad Hosseini, Suvrit Sra:
Geometric Mean Metric Learning. ICML 2016: 2464-2471 - [c40]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization. NIPS 2016: 1145-1153 - [c39]Zelda E. Mariet, Suvrit Sra:
Kronecker Determinantal Point Processes. NIPS 2016: 2694-2702 - [c38]Chengtao Li, Suvrit Sra, Stefanie Jegelka:
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling. NIPS 2016: 4188-4196 - [c37]Hongyi Zhang, Sashank J. Reddi, Suvrit Sra:
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds. NIPS 2016: 4592-4600 - [c36]Zelda Mariet, Suvrit Sra:
Diversity Networks. ICLR (Poster) 2016 - [i26]Hongyi Zhang, Suvrit Sra:
First-order Methods for Geodesically Convex Optimization. CoRR abs/1602.06053 (2016) - [i25]Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Fast DPP Sampling for Nyström with Application to Kernel Methods. CoRR abs/1603.06052 (2016) - [i24]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Fast Incremental Method for Nonconvex Optimization. CoRR abs/1603.06159 (2016) - [i23]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Variance Reduction for Nonconvex Optimization. CoRR abs/1603.06160 (2016) - [i22]Ke Jiang, Suvrit Sra, Brian Kulis:
Combinatorial Topic Models using Small-Variance Asymptotics. CoRR abs/1604.02027 (2016) - [i21]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Fast Stochastic Methods for Nonsmooth Nonconvex Optimization. CoRR abs/1605.06900 (2016) - [i20]Hongyi Zhang, Sashank J. Reddi, Suvrit Sra:
Fast stochastic optimization on Riemannian manifolds. CoRR abs/1605.07147 (2016) - [i19]Zelda Mariet, Suvrit Sra:
Kronecker Determinantal Point Processes. CoRR abs/1605.08374 (2016) - [i18]Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes. CoRR abs/1607.03559 (2016) - [i17]Pourya Zadeh, Reshad Hosseini, Suvrit Sra:
Geometric Mean Metric Learning. CoRR abs/1607.05002 (2016) - [i16]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Frank-Wolfe Methods for Nonconvex Optimization. CoRR abs/1607.08254 (2016) - 2015
- [j11]Suvrit Sra, Reshad Hosseini:
Conic Geometric Optimization on the Manifold of Positive Definite Matrices. SIAM J. Optim. 25(1): 713-739 (2015) - [c35]Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge:
Data modeling with the elliptical gamma distribution. AISTATS 2015 - [c34]Zelda Mariet, Suvrit Sra:
Fixed-point algorithms for learning determinantal point processes. ICML 2015: 2389-2397 - [c33]Reshad Hosseini, Suvrit Sra:
Matrix Manifold Optimization for Gaussian Mixtures. NIPS 2015: 910-918 - [c32]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. NIPS 2015: 2647-2655 - [c31]Sashank J. Reddi, Ahmed Hefny, Carlton Downey, Avinava Dubey, Suvrit Sra:
Large-scale randomized-coordinate descent methods with non-separable linear constraints. UAI 2015: 762-771 - [i15]K. S. Sesh Kumar, Álvaro Barbero Jiménez, Stefanie Jegelka, Suvrit Sra, Francis R. Bach:
Convex Optimization for Parallel Energy Minimization. CoRR abs/1503.01563 (2015) - [i14]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. CoRR abs/1506.06840 (2015) - [i13]Reshad Hosseini, Suvrit Sra:
Manifold Optimization for Gaussian Mixture Models. CoRR abs/1506.07677 (2015) - [i12]Anoop Cherian, Suvrit Sra:
Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices. CoRR abs/1507.02772 (2015) - [i11]Zelda Mariet, Suvrit Sra:
Fixed-point algorithms for determinantal point processes. CoRR abs/1508.00792 (2015) - [i10]Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola:
AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization. CoRR abs/1508.05003 (2015) - [i9]Chengtao Li, Stefanie Jegelka, Suvrit Sra:
Efficient Sampling for k-Determinantal Point Processes. CoRR abs/1509.01618 (2015) - [i8]Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra:
Efficient Structured Matrix Rank Minimization. CoRR abs/1509.02447 (2015) - [i7]Chengtao Li, Suvrit Sra, Stefanie Jegelka:
Bounds on bilinear inverse forms via Gaussian quadrature with applications. CoRR abs/1512.01904 (2015) - 2014
- [j10]Anoop Cherian, Suvrit Sra, Vassilios Morellas, Nikolaos Papanikolopoulos:
Efficient Nearest Neighbors via Robust Sparse Hashing. IEEE Trans. Image Process. 23(8): 3646-3655 (2014) - [c30]Anoop Cherian, Suvrit Sra:
Riemannian Sparse Coding for Positive Definite Matrices. ECCV (3) 2014: 299-314 - [c29]Samaneh Azadi, Suvrit Sra:
Towards an optimal stochastic alternating direction method of multipliers. ICML 2014: 620-628 - [c28]David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf:
Randomized Nonlinear Component Analysis. ICML 2014: 1359-1367 - [c27]Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra:
Efficient Structured Matrix Rank Minimization. NIPS 2014: 1350-1358 - [c26]Matt Wytock, Suvrit Sra, Jeremy Z. Kolter:
Fast Newton methods for the group fused lasso. UAI 2014: 888-897 - [p1]Suvrit Sra:
Tractable Optimization in Machine Learning. Tractability 2014: 202-230 - [i6]David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf:
Randomized Nonlinear Component Analysis. CoRR abs/1402.0119 (2014) - 2013
- [j9]Suvrit Sra, Dmitrii Karp:
The multivariate Watson distribution: Maximum-likelihood estimation and other aspects. J. Multivar. Anal. 114: 256-269 (2013) - [j8]Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon:
A non-monotonic method for large-scale non-negative least squares. Optim. Methods Softw. 28(5): 1012-1039 (2013) - [j7]Anoop Cherian, Suvrit Sra, Arindam Banerjee, Nikolaos Papanikolopoulos:
Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices. IEEE Trans. Pattern Anal. Mach. Intell. 35(9): 2161-2174 (2013) - [c25]Stefanie Jegelka, Francis R. Bach, Suvrit Sra:
Reflection methods for user-friendly submodular optimization. NIPS 2013: 1313-1321 - [c24]Suvrit Sra, Reshad Hosseini:
Geometric optimisation on positive definite matrices for elliptically contoured distributions. NIPS 2013: 2562-2570 - [i5]Stefanie Jegelka, Francis R. Bach, Suvrit Sra:
Reflection methods for user-friendly submodular optimization. CoRR abs/1311.4296 (2013) - [i4]Mikhail A. Langovoy, Suvrit Sra:
Statistical estimation for optimization problems on graphs. CoRR abs/1311.7656 (2013) - 2012
- [j6]Suvrit Sra:
A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of I s (x). Comput. Stat. 27(1): 177-190 (2012) - [j5]Suvrit Sra:
Fast projections onto mixed-norm balls with applications. Data Min. Knowl. Discov. 25(2): 358-377 (2012) - [c23]Suvrit Sra:
A new metric on the manifold of kernel matrices with application to matrix geometric means. NIPS 2012: 144-152 - [c22]Suvrit Sra:
Scalable nonconvex inexact proximal splitting. NIPS 2012: 539-547 - [i3]Suvrit Sra:
Fast projections onto mixed-norm balls with applications. CoRR abs/1204.1437 (2012) - 2011
- [c21]Anoop Cherian, Suvrit Sra, Nikolaos Papanikolopoulos:
Denoising sparse noise via online dictionary learning. ICASSP 2011: 2060-2063 - [c20]Anoop Cherian, Suvrit Sra, Arindam Banerjee, Nikolaos Papanikolopoulos:
Efficient similarity search for covariance matrices via the Jensen-Bregman LogDet Divergence. ICCV 2011: 2399-2406 - [c19]Álvaro Barbero Jiménez, Suvrit Sra:
Fast Newton-type Methods for Total Variation Regularization. ICML 2011: 313-320 - [c18]Suvrit Sra:
Fast Projections onto ℓ1, q -Norm Balls for Grouped Feature Selection. ECML/PKDD (3) 2011: 305-317 - [c17]Suvrit Sra, Anoop Cherian:
Generalized Dictionary Learning for Symmetric Positive Definite Matrices with Application to Nearest Neighbor Retrieval. ECML/PKDD (3) 2011: 318-332 - 2010
- [j4]Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon:
Tackling Box-Constrained Optimization via a New Projected Quasi-Newton Approach. SIAM J. Sci. Comput. 32(6): 3548-3563 (2010) - [c16]Michael Hirsch, Suvrit Sra, Bernhard Schölkopf, Stefan Harmeling:
Efficient filter flow for space-variant multiframe blind deconvolution. CVPR 2010: 607-614 - [c15]Stefan Harmeling, Suvrit Sra, Michael Hirsch, Bernhard Schölkopf:
Multiframe blind deconvolution, super-resolution, and saturation correction via incremental EM. ICIP 2010: 3313-3316 - [c14]Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon:
A scalable trust-region algorithm with application to mixed-norm regression. ICML 2010: 519-526
2000 – 2009
- 2009
- [c13]Stefanie Jegelka, Suvrit Sra, Arindam Banerjee:
Approximation Algorithms for Tensor Clustering. ALT 2009: 368-383 - [c12]Matthias W. Seeger, Suvrit Sra, John P. Cunningham:
Workshop summary: Numerical mathematics in machine learning. ICML 2009: 9 - [c11]Brian Kulis, Suvrit Sra, Inderjit S. Dhillon:
Convex Perturbations for Scalable Semidefinite Programming. AISTATS 2009: 296-303 - [i2]Suvrit Sra:
A Trivial Observation related to Sparse Recovery. CoRR abs/0906.4805 (2009) - 2008
- [j3]Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon:
Fast Projection-Based Methods for the Least Squares Nonnegative Matrix Approximation Problem. Stat. Anal. Data Min. 1(1): 38-51 (2008) - [j2]Justin Brickell, Inderjit S. Dhillon, Suvrit Sra, Joel A. Tropp:
The Metric Nearness Problem. SIAM J. Matrix Anal. Appl. 30(1): 375-396 (2008) - [c10]Suvrit Sra:
Block-Iterative Algorithms for Non-negative Matrix Approximation. ICDM 2008: 1037-1042 - [i1]Stefanie Jegelka, Suvrit Sra, Arindam Banerjee:
Approximation Algorithms for Bregman Co-clustering and Tensor Clustering. CoRR abs/0812.0389 (2008) - 2007
- [c9]Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon:
Information-theoretic metric learning. ICML 2007: 209-216 - [c8]Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon:
Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem. SDM 2007: 343-354 - 2006
- [c7]Suvrit Sra:
Efficient Large Scale Linear Programming Support Vector Machines. ECML 2006: 767-774 - [c6]Suvrit Sra, Joel A. Tropp:
Row-Action Methods for Compressed Sensing. ICASSP (3) 2006: 868-871 - [c5]Arun C. Surendran, Suvrit Sra:
Incremental Aspect Models for Mining Document Streams. PKDD 2006: 633-640 - 2005
- [j1]Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra:
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions. J. Mach. Learn. Res. 6: 1345-1382 (2005) - [c4]Inderjit S. Dhillon, Suvrit Sra:
Generalized Nonnegative Matrix Approximations with Bregman Divergences. NIPS 2005: 283-290 - 2004
- [c3]Inderjit S. Dhillon, Suvrit Sra, Joel A. Tropp:
Triangle Fixing Algorithms for the Metric Nearness Problem. NIPS 2004: 361-368 - [c2]Hyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, Suvrit Sra:
Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data. SDM 2004: 114-125 - 2003
- [c1]Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra:
Generative model-based clustering of directional data. KDD 2003: 19-28
Coauthor Index
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