Preprints

  1. Random Shuffling Beats SGD after Finite Epochs
    Jefferey Z. HaoChen and Suvrit Sra.
    [.bib] [arXiv]
  2. Direct Runge-Kutta Discretization Achieves Acceleration
    Jingzhao Zhang, Aryan Mokhtari, Ali Jadbabaie, and Suvrit Sra. (May 2018)
    [.bib] [arXiv]
  3. New concavity and convexity results for symmetric polynomials and their ratios
    Suvrit Sra. (Mar 2018)
    [.bib] [arXiv]
  4. A critical view of global optimality in deep learning
    Chulhee Yun, Suvrit Sra, and Ali Jadbabaie. (Feb 2018)
    [.bib] [arXiv]
  5. Learning Determinantal Point Processes by Sampling Inferred Negatives
    Zelda Mariet, Mike Gartrell, Suvrit Sra. (Feb 2018)
    [.bib] [arXiv]
  6. On the computation of Wasserstein barycenters of multivariate Gaussians
    Suvrit Sra.
    [.bib] [arXiv]
  7. Frank-Wolfe methods for geodesically convex optimization with application to the matrix geometric mean
    Melanie Weber, Suvrit Sra.
    [.bib] [arXiv]
  8. Unsupervised robust nonparametric learning of hidden community properties
    Mikhail A. Langovoy, Akhilesh Gotmare, Martin Jaggi, Suvrit Sra. (Jul 2017)
    [.bib] [.pdf]
  9. Distributional Adversarial Networks
    Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra. (May 2017)
    [.bib] [arXiv] [Code]
    @article{li2017distributional,
      title={Distributional Adversarial Networks},
      author={Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra},
      journal={arXiv preprint arXiv:1706.09549},
      year={2017}
    }
    
  10. An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization
    Reshad Hosseini, Suvrit Sra. (Jun 2017)
    [.bib] [arXiv]
    @Article{hoSr17,
     author = {Reshad Hosseini and Suvrit Sra},
     title = {{An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic  Riemannian Optimization}},
     journal = {arXiv:1706.03267},
     year = {2017},
     note = {{\it Preprint}}
    }
    
  11. Convex Optimization for Parallel Energy Minimization
    K.S. Sesh Kumar, Álvaro J. Barbero, Suvrit Sra, Stefanie Jegelka, and Francis Bach.
    [.bib] [arXiv]
    @Article{sesh15,
     author = {K.S. Sesh Kumar and \'Alvaro Barbero and Stefanie Jegelka and  Suvrit Sra and Francis Bach},
     title = {{Convex Optimization for Parallel Energy Minimization}},
     journal = {arXiv:1503.01563},
     year = {2015},
    }
    
  12. Modular proximal optimization with application to total variation regularization.
    Álvaro J. Barbero, Suvrit Sra. submitted Nov 2013; (v2 Oct. 2014, v3 2016)
    [.bib] [arXiv] [.pdf]
    @Article{barSra14,
     author = {\'Alvaro J. Barbero and Suvrit Sra},
     title = {{Modular proximal optimization for multidimensional total-variation regularization}},
     journal = {arXiv:1411.0589},
     year = {2014},
     note = {\it Submitted}
    }
    
  13. 2018

  14. On Geodesically Convex Formulations for the Brascamp-Lieb Constant
    Suvrit Sra, Nisheeth K. Vishnoi and Ozan Yıldız
    21st International Conference on Approximation Algorithms for Combinatorial Optimization Problems (APPROX'2018)
    [.bib] [arXiv]
  15. An Estimate Sequence for Geodesically Convex Optimization
    Hongyi Zhang, Suvrit Sra.
    Conference on Learning Theory (COLT) 2018
    [.bib] [arXiv]
  16. Non-Linear Temporal Subspace Representations for Activity Recognition
    Anoop Cherian, Suvrit Sra, Stephen Gould, Richard Hartley.
    Computer Vision and Pattern Recognition (CVPR) 2018
    [.bib] [arXiv]
    @Article{chSrHa18,
     author = {Anoop Cherian and Suvrit Sra and Stephen Gould and Richard Hartley},
     title = {Non-Linear Temporal Subspace Representations for Activity Recognition},
     journal = {arXiv:1803.11064},
     year = {2018},
     note = {{\it CVPR 2018}}
    }
    
  17. Directional Statistics in Machine Learning: a Brief Review
    Suvrit Sra.
    In Applied directional statistics
    [.bib] [.pdf]
  18. Global optimality conditions for deep neural networks
    Chulhee Yun, Suvrit Sra, Ali Jadbabaie.
    International Conference on Learning Representations (ICLR 2018)
    [.bib] [arXiv]
    @Article{yuSrJa17,
    author = {Chulhee Yun and Suvrit Sra and Ali Jadbabaie},
    title = {Global optimality conditions for deep neural networks},
    journal = {arXiv:1707:02444},
    year = {2018},
    note = {{\it ICLR 2018}}
    }
    
  19. A Generic Approach for Escaping Saddle points
    S. Reddi, M. Zaheer, S. Sra, F. Bach, B. Poczos, R. Salakhutdinov, A. Smola
    Artificial Intelligence and Statistics (AISTATS 2018)
    [.bib] [arXiv]
  20. 2017

  21. Inequalities via symmetric polynomial majorization
    Suvrit Sra
    Accepted to Proceedings American Mathematical Society (PAMS) (Nov 2017)
    [.bib] [arXiv] [.pdf]
    @Article{sra15esym,
     author = {Suvrit Sra},
     title = {Inequalities via symmetric polynomial majorization},
     journal = {PAMS},
     year = {2015},
     note = {{\it arXiv:1509.05902}}
    }
    
  22. Elementary Symmetric Polynomials for Optimal Experimental Design
    Zelda Mariet, Suvrit Sra
    Advances in Neural Information Processing Systems (NIPS) 2017
    [.bib] [arXiv]
  23. Polynomial Time Dual Volume Sampling
    Chengtao Li, Stefanie Jegelka, Suvrit Sra
    Advances in Neural Information Processing Systems (NIPS) 2017
    [.bib] [arXiv]
  24. Combinatorial Topic Models using Small–Variance Asymptotics.
    Ke Jiang, Suvrit Sra, Brian Kulis
    Artificial Intelligence and Statistics (AISTATS) 2017
    [.bib] [arXiv]
  25. 2016

  26. Kronecker Determinantal Point Processes
    Zelda Mariet, Suvrit Sra
    Advances in Neural Information Processing Systems (NIPS) 2016
    [.bib] [arXiv]
  27. Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization
    Sashank Reddi, Suvrit Sra, Barnabas Poczos, Alex Smola
    Advances in Neural Information Processing Systems (NIPS) 2016
    [.bib] [.pdf] [arXiv]
  28. Fast stochastic optimization on Riemannian manifolds
    Hongyi Zhang, Sashank Reddi, Suvrit Sra
    Advances in Neural Information Processing Systems (NIPS) 2016
    [.bib] [arXiv]
  29. Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling
    Chengtao Li, Stefanie Jegelka, Suvrit Sra
    Advances in Neural Information Processing Systems (NIPS) 2016
    [.bib] [arXiv]
  30. Stochastic Frank-Wolfe Methods for Nonconvex Optimization
    Sashank Reddi, Suvrit Sra, Barnabas Poczos, Alex Smola
    54th Annual Allerton Conference on Communication, Control, and Computing. Dec 2016
    [.bib] [.pdf] [arXiv]
  31. Riemannian dictionary learning and sparse coding for positive definite matrices
    Anoop Cherian, Suvrit Sra
    IEEE Trans. Neural Networks and Learning Systems (TNNLS) 2016.
    [.bib] [.pdf] [arXiv]
    @Article{cheSra15,
     author = {Anoop Cherian and Suvrit Sra},
     title = {{Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices}},
     journal = {arXiv:1507.02772},
     mon     = {Jul.},
     year = {2015},
    }
    
  32. Fast incremental method for smooth nonconvex optimization
    Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alexander J. Smola
    IEEE Conference on Decision and Control (CDC), Dec 2016.
    [.bib] [arXiv]
  33. Positive Definite Matrices: Data Representation and Applications to Computer Vision
    Anoop Cherian, Suvrit Sra.
    Book chapter in Algorithmic Advances in Riemannian Geometry and Applications, Springer, 2016.
    [.bib] [.pdf]
  34. Geometric Optimization in Machine Learning
    Suvrit Sra, Reshad Hosseini
    Book chapter in Algorithmic Advances in Riemannian Geometry and Applications, Springer, 2016.
    [.bib] [.pdf]
  35. First-order methods for geodesically convex optimization
    Hongyi Zhang, Suvrit Sra
    Conference on Learning Theory (COLT 2016)
    [.bib] [.pdf] [arXiv]
    @Article{zhangSra16a,
     author = {Hongyi Zhang and Suvrit Sra},
     title = {First-order methods for geodesically convex optimization},
     journal = {arXiv:1602.06053},
     year = {2016},
     note = {{\it Preprint}}
    }
    
  36. Stochastic variance reduction for nonconvex optimization
    Sashank Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alexander J. Smola
    International Conference on Machine Learning (ICML 2016)
    [.bib] [.pdf] [arXiv]
    @Article{reddi2016a,
     author = {Sashank Reddi and Ahmed Hefny and Suvrit Sra and Barnabas Poczos and Alexander J. Smola},
     title = {Stochastic variance reduction for nonconvex optimization},
     journal = {arXiv:1603.xxxx},
     year = {2016},
     note = {{\it Preprint}}
    }
    
  37. Geometric Mean Metric Learning.
    Pourya H. Zadeh, Reshad Hosseini, Suvrit Sra
    International Conference on Machine Learning (ICML 2016)
    [.bib] [.pdf]
  38. Fast DPP Sampling for Nyström with Application to Kernel Methods
    Chengtao Li, Stefanie Jegelka, Suvrit Sra
    International Conference on Machine Learning (ICML 2016)
    [.bib] [.pdf] [arXiv]
  39. Gaussian quadrature for matrix inverse forms with applications
    Chengtao Li, Suvrit Sra, Stefanie Jegelka
    International Conference on Machine Learning (ICML 2016)
    [.bib] [.pdf] [arXiv]
  40. Asynchronous Parallel Block-Coordinate Frank-Wolfe
    Y.-X. Wang, V. Sadhanala, W. Dai, W. Neiswanger, Suvrit Sra, E. P. Xing
    International Conference on Machine Learning (ICML 2016)
    [.bib] [.pdf] [arXiv]
    @Article{wangSaSra14,
     author = {Y.-X. Wang and V. Sadhanala and W. Dai and W. Neiswanger and Suvrit Sra and E. P. Xing},
     title = {{Asynchronous Parallel Block-Coordinate Frank-Wolfe}},
     journal = {arXiv:1409.6086},
     year = {2014},
    }
    
  41. On the Matrix Square Root via Geometric Optimization
    Suvrit Sra
    Electronic Journal of Linear Algebra (ELA) (May 2016).
    [.bib] [arXiv] [.pdf]
    @Article{sraRoot,
     author = {Suvrit Sra},
     title = {{On the matrix square root and geometric optimization}},
     journal = {arXiv:1507.08366},
     year = {2015},
     note = {\it Preprint}
    }
    
  42. Entropic Metric Alignment for Correspondence Problems
    Justin Solomon, Gabriel Peyré, Vladimir Kim, Suvrit Sra
    ACM SIGGRAPH 2016
    [.bib] [.pdf] [supplement]
    @Article{solomon16,
     author = {Justin Solomon and Gabriel Peyré and Vladimir Kim and Suvrit Sra},
     title = {{Entropic Metric Alignment for Correspondence Problems}},
     journal = {ACM SIGGRAPH},
     year = {2016},
    }
    
  43. Inference and mixture modelling with the Elliptical Gamma Distribution
    Reshad Hosseini, Suvrit Sra, Lucas Theis, Matthias Bethge
    Computational Statistics and Data Analysis (CSDA). Feb 2016
    [.bib] [arXiv]
    @Article{hoSra14,
     author = {Reshad Hosseini and Suvrit Sra and L. Theis and M. Bethge},
     title = {Statistical inference with the Elliptical Gamma Distribution},
     journal = {arXiv:1410.4812},
     year = {2014},
     note = {{\it Accepted to CSDA}},
    }
    
  44. Diversity Networks: Neural Network Compression Using Determinantal Point Processes
    Zelda Mariet, Suvrit Sra
    International Conference on Learning Representations (ICLR 2016)
    [.bib] [arXiv]
    @Article{marietSra15b,
     author = {Zelda Mariet and Suvrit Sra},
     title = {Diversity Networks},
     journal = {arXiv:1511.05077},
     year = {2015},
     note = {\it Preprint}
    }
    
  45. The sum of squared logarithms inequality in arbitrary dimensions
    Lev Borisov, Patrizio Neff, Suvrit Sra, Christian Thiel
    Linear Algebra and its Applications (LAA) Jan 2016.
    [.bib] [arXiv]
    @Article{boNes15,
     author = {Lev Borisov and Patrizio Neff and Suvrit Sra and Christian Thiel},
     title = {{The sum of squared logarithms inequality in arbitrary dimensions}},
     journal = {arXiv:1508.04039},
     year = {2015},
     note = {\it Preprint}
    }
    
  46. Efficient sampling for k-determinantal point processes
    Chengtao Li, Stefanie Jegelka, Suvrit Sra
    Artificial Intelligence and Statistics (AISTATS 2016)
    [.bib] [arXiv]
    @Article{ctli15,
     author = {Chengtao Li and Stefanie Jegelka and Suvrit Sra},
     title = {Efficient sampling for k-determinantal point processes},
     journal = {arXiv:1509.01618},
     year = {2015},
    }
    
  47. AdaDelay: Delay sensitive distributed stochastic convex optimization
    Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola
    Artificial Intelligence and Statistics (AISTATS 2016)
    [.bib] [arXiv]
    @Article{sra15.distrib,
     author = {Suvrit Sra and Adams Wei Yu and Mu Li and Alexander J. Smola},
     title = {AdaDelay: Delay sensitive distributed stochastic convex optimization},
     journal = {arXiv:1508.05003},
     year = {2015},
     note = {\it Preprint}
    }
    
  48. 2015

  49. Positive Definite Matrices and the S-Divergence
    Suvrit Sra
    Proceedings of the American Mathematical Society (PAMS). Oct 2015.
    [.bib] [arXiv] [.pdf]
    @Article{srasdiv,
     author = {Suvrit Sra},
     title = {{Positive Definite Matrices and the S-Divergence}},
     journal = {Proceedings of the American Mathematical Society},
     year = {2015},
     mon  = {Sep}
     note = {arXiv:1110.1773v4}
    }
    
  50. Matrix Manifold Optimization for Gaussian Mixtures
    Reshad Hosseini, Suvrit Sra
    Advances in Neural Information Processing Systems (NIPS 2015)
    [.bib] [arXiv] .pdf]
    @Article{hoSr15b,
     author = {Reshad Hosseini and Suvrit Sra},
     title = {{Manifold optimization for mixture modeling}},
     journal = {arXiv:1506.07677},
     year = {2015},
     note = {\it Submitted}
    }
    
  51. Asynchronous variance reduced stochastic gradient descent
    Sashank Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alexander J. Smola
    Advances in Neural Information Processing Systems (NIPS 2015)
    [.bib] [arXiv]
    @Article{red15,
     author = {Sashank Reddy and Ahmed Hefny and Suvrit Sra and Barnabas Poczos and Alexander J. Smola},
     title = {Variance reduction in stochastic gradient},
     journal = {arXiv:1506.06840},
     year = {2015},
    }
    
  52. On inequalities for normalized Schur functions
    Suvrit Sra
    European Journal of Combinatorics. (submitted Feb 2015; accepted May 2015)
    [.bib] [arXiv] [.pdf]
    @Article{sra15a,
     author = {Suvrit Sra},
     title = {{On inequalities for normalized Schur functions}},
     journal = {European J. Combinatorics},
     volume = {Volume 51},
     year = {2016},
     pages = {492-–494},
     mon  = jan,
    }
    
  53. A proof of Thompson's determinantal inequality
    Minghua Lin, Suvrit Sra
    Mathematical Notes. (accepted Jun 2015).
    [.bib] [arXiv]
    @Article{linSra14,
     author = {Minghua Lin and Suvrit Sra},
     title = {{Complete strong superadditivity of generalized matrix functions}},
     journal = {Mathematical Notes},
     year = {2015},
    }
    
  54. Hlawka-Popoviciu inequalities on positive definite tensors
    Wolfgang Berndt, Suvrit Sra
    Linear Algebra and its Applications (accepted Feb 2015)
    [.bib] [arXiv] [.pdf]
    @Article{berSra15,
     author = {Wolfgang Berndt and Suvrit Sra},
     title = {{Hlawka-Popoviciu inequalities on positive definite tensors}},
     journal = {Linear Algebra and its Applications},
     volume = {486},
     number = {1},
     pages = {317--327},
     year = {2015},
     note = {arXiv:1411.0065}
    }
    
  55. Large-scale randomized-coordinate descent methods with non-separable linear constraints
    Sashank J. Reddi, Ahmed Hefny, Carlton Downey, Abhinava Dubey, Suvrit Sra
    Uncertainty in Artificial Intelligence (UAI 2015)
    [.bib] [arXiv] [.pdf]
    @Article{reHeSra14,
     author = {Sashank J. Reddi and Ahmed Hefny and Carlton Downey and Avinava Dubey and Suvrit Sra},
     title = {{Large-scale randomized-coordinate descent methods with non-separable linear constraints}},
     journal = {arXiv:1409.2617v3},
     mon     = {Oct.},
     year = {2014},
    }
    
  56. Fixed-point algorithms for learning determinantal point processes
    Zelda Mariet, Suvrit Sra
    International Conference on Machine Learning (ICML 2015)
    [.bib] [arXiv] [.pdf]
    @Inproceedings{marSra15,
     author = {Zelda Mariet and Suvrit Sra},
     title = {{Fixed-point algorithms for learning determinantal point processes}},
     booktitle = {International Conference on Machine Learning (ICML)},
     mon     = {Jun},
     year = {2015},
    }
    
  57. Conic geometric optimisation on the manifold of positive definite matrices
    Suvrit Sra, Reshad Hosseini
    SIAM Journal on Optimization (SIOPT) (accepted Jan 2015).
    [.bib] [arXiv] [.pdf]
    @Article{sraHo15,
     author = {Suvrit Sra and Reshad Hosseini},
     title = {{Conic Geometric Optimization on the Manifold of Positive Definite Matrices}},
     volume = {25},
     number = {1},
     pages  = {713--739},
     journal = {SIAM J. Optimization (SIOPT)},
     year = {2015},
    }
    
  58. Data Modeling with the Elliptical Gamma Distribution
    Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge
    Artificial Intelligence and Statistics (AISTATS 2015)
    [.bib] [arXiv] [.pdf]
    @InProceedings{hoSra15,
     author = {Reshad Hosseini and Suvrit Sra and L. Theis and M. Bethge},
     title = {Statistical inference with the Elliptical Gamma Distribution},
     booktitle = {Artificial Intelligence and Statistics (AISTATS)},
     year = {2015},
     volume = 18,
    }
    
  59. 2014

  60. Efficient Structured Matrix Rank Minimization
    Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra
    Advances in Neural Information Processing Systems (NIPS 2014)
    [.bib] [arXiv] [.pdf]
    @inproceedings{adams14,
     author    = {Adams Wei Yu and Wanli Ma and Yaoliang Yu and Jaime G. Carbonell and Suvrit Sra},
     title     = {Efficient Structured Matrix Rank Minimization},
     booktitle = {NIPS},
     year      = {2014},
     note      = {arXiv:1509.02447}
    }
                    
  61. Riemannian sparse coding for positive definite matrices
    Anoop Cherian, Suvrit Sra
    European Conference on Computer Vision (ECCV 2014)
    [.bib] [.pdf]
    @inproceedings{chSra14,
      title={Riemannian sparse coding for positive definite matrices},
      author={Anoop Cherian and Suvrit Sra},
      booktitle={ECCV 2014},
      pages={299--314},
      year={2014},
      publisher={Springer}
    }
                    
  62. Fast Newton methods for the group fused lasso
    Matt Wytock, Suvrit Sra, Zico Kolter
    Uncertainty in Artificial Intelligence (UAI 2014).
    [.bib] [.pdf]
    @inproceedings{wytock,
      title={Fast Newton methods for the group fused lasso},
      author={Matt Wytock and Suvrit Sra and Zico J. Kolter},
      booktitle={Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence},
      year={2014}
    }
                    
  63. Efficient nearest neighbors via robust sparse hashing
    Anoop Cherian, Suvrit Sra, Vassilios Morellas, and Nikos Papanikolopoulos
    IEEE Transactions on Image Processing, 23(8); 2014.
    [.bib] [arXiv] [.pdf]
    @article{chSra14b,
      title={Efficient nearest neighbors via robust sparse hashing},
      author={Anoop Cherian and Suvrit Sra and Vassilios Morellas and Nikolaos Papanikolopoulos},
      journal={IEEE Transactions on Image Processing},
      volume={23},
      number={8},
      pages={3646--3655},
      year={2014},
      publisher={IEEE}
    }
                    
  64. Randomized Nonlinear Component Analysis
    David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, and Bernhard Schölkopf
    International Conference on Machine Learning (ICML 2014)
    [.bib] [arXiv]
    @Inproceedings{lopez,
      title={{Randomized Nonlinear Component Analysis}},
      author={David Lopez-Paz and Suvrit Sra and Alex Smola and Zoubin Ghahramani and Bernhard Schoelkopf},
      booktitle={Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
      pages={1359--1367},
      year={2014}
    }
                    
  65. Towards stochastic alternating direction method of multipliers
    Samaneh Azadi and Suvrit Sra
    International Conference on Machine Learning (ICML 2014)
    [.bib] [.pdf]
    @inproceedings{azadi,
      title={Towards an optimal stochastic alternating direction method of multipliers},
      author={Samaneh Azadi and Suvrit Sra},
      booktitle={Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
      pages={620--628},
      year={2014}
    }
                    
  66. Nonconvex proximal splitting: batch and incremental algorithms
    Suvrit Sra
    Invited book chapter: Regularization, Optimization, Kernels, and Support Vector Machines. (eds: J. A.K. Suykens, M. Signoretto, A. Argyriou. Mar 2014.
    [.bib] [.pdf] [arXiv] [MPI-TR]
    @InCollection{sra.ncprox,
      author =       {Suvrit Sra},
      title =        {Nonconvex proximal splitting: batch and incremental algorithms},
      booktitle =    {Regularization, Optimization, Kernels, and Support Vector Machines},
      publisher =    {Cambridge University Press},
      month     =    mar,
      year =         2014,
      editor =       {J. A. K. Suykens and M. Signoretto and A. Argyriou},
    }
                    
  67. 2013

  68. Tractable large-scale optimization in machine learning
    Suvrit Sra
    Invited book chapter: Tractability Practical Approaches to Hard Problems. (eds: L. Bordeaux, Y. Hamadi, P. Kohli); Aug 2013.
    [.bib] [.pdf]
    @InCollection{sra.optml,
      author =       {Suvrit Sra},
      title =        {Tractable Large-Scale Optimization in Machine Learning},
      booktitle =    {Advances in Tractability},
      publisher =    {Cambridge University Press},
      month     =    dec,
      year =         2013,
      editor =       {L. Bordeaux and Y. Hamadi and P. Kohli and R. Mateescu},
      note =         {29 pages},
    }
                    
  69. Geometric optimisation on positive definite matrices with application to elliptically contoured distributions
    Suvrit Sra and Reshad Hosseini
    Advances in Neural Information Processing Systems (NIPS 2013)
    [.bib] [arXiv] [.pdf]
    @InProceedings{sraHo13,
      author =       {Suvrit Sra and Reshad Hosseini},
      title =        {{Geometric optimisation on positive definite matrices with application to elliptically contoured distributions}},
      booktitle =    {Advances in Neural Information Processing Systems (NIPS)},
      year =         2013,
      month =        dec,
    }
                    
  70. Reflection methods for user-friendly submodular optimization
    Stefanie Jegelka, Francis Bach, and Suvrit Sra
    Advances in Neural Information Processing Systems (NIPS 2013)
    [.bib] [arXiv] [.pdf]
    @inproceedings{jeg13,
      title={Reflection methods for user-friendly submodular optimization},
      author={Stefanie Jegelka and Francis  Bach and Suvrit Sra},
      booktitle={Advances in Neural Information Processing Systems},
      pages={1313--1321},
      year={2013}
    }
                    
  71. Correlation matrix nearness and completion under observation uncertainty
    Carlos M. Alaiz, Francesco Dinuzzo, and Suvrit Sra
    IMA Journal of Numerical Analysis Oct 2013.
    [.bib] [.pdf] [preprint]
    @Article{carlos,
      author =       {Carlos M. Ala\'iz and Francesco Dinuzzo and Suvrit Sra},
      title =        {Correlation matrix nearness and completion under observation uncertainty},
      journal =      {IMA Journal of Numerical Analysis},
      year =         {2013},
      month =        {Oct.},
      note =         {16 pages},
    }
                    
  72. 2012

  73. Jensen-Bregman LogDet Divergence for Efficient Similarity Computations on Positive Definite Tensors
    Anoop Cherian, Suvrit Sra, Arindam Banerjee, and Nikos Papanikolopoulos
    IEEE Transactions Pattern Analysis and Machine Intelligence (TPAMI) Dec. 2012.
    [.bib] [.pdf]
    @Article{chSra.pami,
      author =       {Anoop Cherian and Suvrit Sra and Arindam Banerjee and Nikolaos Papanikolopoulos},
      title =        {{Jensen-Bregman LogDet Divergence with Application to Efficient Similarity  Search for Covariance Matrices}},
      journal =      {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
      year =         2012,
      month =        {Dec.},
      note =         {14 pages},
    }
                    
  74. A new metric on the manifold of kernel matrices with application to matrix geometric means
    Suvrit Sra
    Advances of Neural Information Processing Systems (NIPS 2012)
    [.bib] [.pdf]
    @inproceedings{sra12.nips,
      title={A new metric on the manifold of kernel matrices with application to matrix geometric means},
      author={Suvrit Sra},
      booktitle={Advances in Neural Information Processing Systems},
      pages={144--152},
      year={2012}
    }
                    
  75. Scalable nonconvex inexact proximal splitting
    Suvrit Sra
    Advances of Neural Information Processing Systems (NIPS 2012)
    [.bib] [.pdf]
  76. The multivariate Watson distribution: Maximum-likelihood estimation and other aspects
    Suvrit Sra, Dmitrii B. Karp
    Journal of Multivariate Analysis (accepted 2012)
    [.bib] [arXiv] [.pdf] [.pdf]
  77. Explicit eigenvalues of certain scaled trigonometric matrices
    Suvrit Sra
    Linear Algebra and its Applications (accepted Jul 2012)
    [.bib] [arXiv] [.pdf]
  78. Fast projection onto mixed-norm balls with applications
    Suvrit Sra
    Data Minining and Knowledge Discovery. 2012
    [.bib] [arXiv] [.pdf]
  79. 2011

  80. A non-monotonic method for large-scale non-negative least squares
    Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
    Optimization Methods and Software (accepted: Dec. 2011)
    [.bib] [.pdf]
  81. Efficient Similarity Search for Covariance Matrices via the Jensen-Bregman LogDet Divergence
    Anoop Cherian, Suvrit Sra, Arindam Banerjee, and Nikos Papanikolopoulos
    International Conference on Computer Vision (ICCV) (2011)
    [.bib] [.pdf]; [Bugfix .pdf]
  82. Generalized Dictionary Learning for Symmetric Positive Definite Matrices with Application to Nearest Neighbor Retrieval
    Suvrit Sra, Anoop Cherian
    European Conf. on Machine Learning (ECML) (2011)
    [.bib] [.pdf]
  83. Fast projections onto L1,q-norm balls for grouped feature selection
    Suvrit Sra
    European Conference on Machine Learning (ECML 2011). Best Paper Runner Up
    [.bib] [.pdf]
  84. Fast Newton-type Methods for Total-Variation with Applications
    Álvaro J. Barbero, Suvrit Sra
    International Conference on Machine Learning (ICML 2011)
    [.bib] [.pdf]
  85. A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of I_s(x)
    Suvrit Sra
    Computational Statistics 2011.
    [.bib] [.pdf]
  86. Optimization for Machine Learning
    Suvrit Sra, Sebastian Nowozin, Stephen J. Wright
    MIT Press, 2011.
    [MIT Press] [Amazon] [Barnes and Noble]
  87. Projected Newton-type methods in machine learning
    Mark Schmidt, Dongmin Kim, Suvrit Sra
    In: "Optimization for Machine Learning": MIT Press, 2011.
    [.bib] [.pdf]
  88. Online Multi-frame Blind Deconvolution with Super-resolution and Saturation Correction
    Michael Hirsch, Stefan Harmeling, Suvrit Sra, Bernhard Schölkopf
    Astronomy & Astrophysics Feb (2011)
    [.bib] [.pdf]
  89. Denoising sparse noise via online dictionary learning
    Anoop Cherian, Suvrit Sra, Nikos Papanikolopoulos
    IEEE Conference on Speech Acoustics and Signal Processing (ICASSP 2011)
    [.bib] [.pdf]
  90. 2010

  91. Sparse inverse covariance estimation using an adaptive gradient method
    Suvrit Sra and Dongmin Kim
    [.bib] [arXiv]
  92. Tackling box-constrained convex optimization via a new projected quasi-Newton approach
    Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
    SIAM Journal on Scientific Computing (SISC). Oct 2010
    [.bib] [.pdf
  93. A scalable trust-region algorithm with application to mixed-norm regression
    Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
    Interational Conference on Machine Learning (ICML 2010)
    [.bib] [.pdf]
  94. Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction via Incremental EM
    Stefan Harmeling, Suvrit Sra, Michael Hirsch, Bernhard Schölkopf
    IEEE International Conference on Image Processing (ICIP 2010)
    [.bib] [.pdf]
  95. Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution
    Michael Hirsch, Suvrit Sra, Bernhard Schölkopf, Stefan Harmeling
    IEEE Conference Computer Vision & Pattern Recognition (CVPR 2010)
    [.bib] [.pdf]
  96. Sparse nonnegative matrix approximation: new formulations and algorithms
    Rashish Tandon and Suvrit Sra
    MPI Technical Report #193. Sep 2010
    [.bib] [.pdf]
  97. Fast algorithms for total-variation based optimization
    Alvaro J. Barbero and Suvrit Sra
    MPI Technical Report #194. Aug 2010
    [.bib] [.pdf]
  98. Generalized proximity and projection with norms and mixed-norms
    Suvrit Sra
    MPI Technical Report #192. May 2010
    [.bib] [.pdf]
  99. 2009

  100. Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution
    Michael Hirsch, Suvrit Sra, Bernhard Schölkopf and Stefan Harmeling
    MPI Technical Report #188 Nov 2009
    [.bib] [.pdf]
  101. Text Clustering with Mixture of von Mises-Fisher Distributions
    Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra
    In: "Text Mining: Theory, Applications, and Visualization" eds. A. N. Srivastava and M. Sahami, CRC Press. 2009.
    [.bib] [.pdf]
  102. Approximation Algorithms for Tensor clustering
    Stefanie Jegelka, Suvrit Sra, Arindam Banerjee
    Algorithmic Learning Theory (ALT 2009).
    [.bib] [arXiv]
  103. Online Blind Deconvolution for Astronomy
    Stefan Harmeling, Michael Hirsch, Suvrit Sra, Bernhard Schölkopf
    IEEE Interational Conferemce on Computational Photography (ICCP 2009)
    [.bib] [.pdf]
  104. A new non-monotonic algorithm for PET image reconstruction
    Suvrit Sra, Dongmin Kim, Inderjit S. Dhillon, Bernhard Schölkopf
    IEEE Nuclear Science Symposium / Medical Imaging Conf. (NSS/MIC 2009)
    [.bib] [Conference Record M03-2: ]
  105. Scalable Semidefinite Programming using Convex Perturbations
    Brian Kulis, Suvrit Sra, Inderjit S. Dhillon
    Artificial Intelligence and Statistics (AISTATS 2009)
    [.bib] [.pdf]
  106. 2008

  107. Block-Iterative Algorithms for Non-negative Matrix Approximation
    Suvrit Sra
    IEEE International Conference on Data Mining (ICDM 2008)
    [.bib] [.pdf]
  108. The Metric Nearness Problem
    Justin Brickell, Inderjit S. Dhillon, Suvrit Sra, Joel A. Tropp
    >SIAM Journal on Matrix Analysis and Applications (SIMAX). 30(1). pp. 375--396 (2008).
    SIAM Outstanding Paper Prize 2011 -- across SIAM Journals in the three years 2008--2010
    [.bib] [.pdf]
  109. Approximation Algorithms for Bregman Clustering Co-clustering and Tensor Clustering
    Suvrit Sra, Stefanie Jegelka, and Arindam Banerjee
    MPI Technical Report #177 2008.
    [.bib] [.pdf]
  110. Block iterative algorithms for non-negative matrix approximation
    Suvrit Sra
    MPI Technical Report #176 2008
    [.bib] [.pdf]
  111. Non-monotonic Poisson Likelihood Maximization
    Suvrit Sra, Dongmin Kim, and Bernhard Schölkopf
    MPI Technical Report #170. Jun 2008
    [.bib] [.pdf]
  112. A New Non-monotonic Gradient Projection Method for the Non-negative Least Squares Problem
    Dongmin Kim, Suvrit Sra, and Inderjit S. Dhillon
    Computer Sciences, University of Texas at Austin, TR-08-28.
    [.bib] [.pdf]
  113. Some links below are broken; under construction

    2007

  114. Matrix Nearness Problems in Data Mining
    Suvrit Sra
    Ph.D. Thesis. University of Texas at Austin. Aug. 2007
    Thesis: .pdf [.bib] [arXiv] [.pdf]
  115. Information-theoretic Metric Learning
    Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon
    International Conference on Machine Learning (ICML) 2007.; (Best Student Paper)
    Paper: .pdf [.bib] [arXiv] [.pdf]
  116. Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem
    Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
    SIAM International Conference on Data Mining (SDM) 2007; Best of SDM 2007 papers
    Paper: .pdf [.bib] [arXiv] [.pdf]
  117. Fast Projection-Based Methods for the Least Squares Nonnegative Matrix Approximation Problem
    Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
    in Statistical Analysis and Data Mining vol. 1 pp. 38-51 (2007);
    [.pdf]; [author PDF] [.bib]
  118. Scalable Semidefinite Programming using Convex Perturbations
    Brian Kulis, Suvrit Sra, Stefanie Jegelka, and Inderjit S. Dhillon
    Comp. Sci., Univ. of Texas at Austin, TR-07-47, Sep. 2007;
    Paper: .pdf [.bib] [arXiv] [.pdf]
  119. A New Projected Quasi-Newton Approach for solving the Nonnegative Least-Squares Problem
    Dongmin Kim, Suvrit Sra, and Inderjit S. Dhillon
    Comp. Sci., Univ. of Texas at Austin, TR-06-54, May 2007;
    Paper: .pdf [.bib] [arXiv] [.pdf]
  120. Modeling data using directional distributions: Part II
    Suvrit Sra, Prateek Jain, and Inderjit S. Dhillon
    Comp. Sci., Univ. of Texas at Austin, TR-07-05, Feb. 2007;
    Paper: .pdf [.bib] [arXiv] [.pdf]
  121. 2006--2003

  122. Information-theoretic Metric Learning
    Jason V. Davis, Brian Kulis, Suvrit Sra, and Inderjit S. Dhillon
    NIPS 2006 Workshop on learning to compare examples, Dec. 2006;
    Paper: .pdf [.bib] [arXiv] [.pdf]
  123. Incremental Aspect Models for Mining Document Streams
    Arun Surendran, Suvrit Sra
    in Principles and Practice of Knowledge Discovery in Databases (PKDD) 2006;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  124. Efficient Large Scale Linear Programming Support Vector Machines
    Suvrit Sra
    in European Conference on Machine Learning (ECML) 2006.;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  125. Row-action Methods for Compressed Sensing
    Suvrit Sra, Joel A. Tropp
    in International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2006.;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  126. Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
    Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra
    in Journal of Machine Learning Research (JMLR) vol. 6 pp. 1345-1382 (2005);
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  127. Generalized Nonnegative Matrix Approximations with Bregman Divergences
    Inderjit S. Dhillon, Suvrit Sra
    in Advances Neural Information Processing Systems (NIPS) 2005.;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  128. Minimum Sum Squared Residue based Co-clustering of Gene Expression data
    Hyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, Suvrit Sra
    in SIAM International Conference on Data Mining (SDM) 2004;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  129. Triangle Fixing Algorithms for the Metric Nearness Problem
    Inderjit S. Dhillon, Suvrit Sra, J. A. Tropp
    Advances in Neural Information Processing Systems (NIPS) 2004;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  130. Generative Model-Based Clustering of Directional Data
    Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra
    International Conference on Knowledge Discovery and Data Mining (KDD) 2003.;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  131. Nonnegative Matrix Approximation: Algorithms and Applications
    Suvrit Sra and Inderjit S. Dhillon
    Comp. Sci., Univ. of Texas at Austin TR-06-27, Jun 2006;
    Paper: .pdf [.bib] [arXiv] [.pdf]
  132. Generalized Nonnegative Matrix Approximations using Bregman Divergences
    Inderjit S. Dhillon and Suvrit Sra
    Comp. Sci., Univ. of Texas at Austin TR-05-31, Jun 2005;
    Paper: .pdf [.bib] [arXiv] [.pdf]
  133. Triangle Fixing Algorithms for the Metric Nearness Problem
    Inderjit S. Dhillon, Suvrit Sra, and Joel A. Tropp
    Comp. Sci., Univ. of Texas at Austin TR-04-22, Jun 2004;
    Paper: .pdf [.bib] [arXiv] [.pdf]
  134. The Metric Nearness Problem with Applications
    Inderjit S. Dhillon, Suvrit Sra, and Joel A. Tropp
    Comp. Sci., Univ. of Texas at Austin TR-03-23, July 2003;
    Paper: .ps.gz [.bib] [arXiv] [.pdf]
  135. Expectation Maximization for Clustering on Hyperspheres
    Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, and Suvrit Sra
    Comp. Sci., Univ. of Texas at Austin TR-03-07, Feb. 2003;
    Paper: .ps.gz [.bib] [arXiv] [.pdf]
  136. Modeling Data using Directional Distributions
    Inderjit S. Dhillon and Suvrit Sra
    Comp. Sci., Univ. of Texas at Austin TR-03-06, Jan. 2003;
    Paper: .ps.gz [.bib] [arXiv] [.pdf]
Tidbits
  • Suvrit Sra. Explicit diagonalization of an anti-triangular stochastic matrix Nov 2014.
    @Article{sraDiag,
    author = {Suvrit Sra},
    title = {{Explicit diagonalization of an anti-triangular Ces\'aro matrix}},
    journal = {arXiv:1411.4107},
    mon     = {Nov.},
    year = {2014},
    }
                        
  • Suvrit Sra. A trivial remark on Cauchy-Schwarz Feb 2014.
  • ILAS Image #47. Ex.[#1]
  • ILAS Image #48. Ex.[#2]
  • ILAS Image #49. Ex.[#3]
  • ILAS Image #51. Ex.[#4]
Workshops Organized
Collaborators