Preprints

  1. Mikhail A. Langovoy, Akhilesh Gotmare, Martin Jaggi, Suvrit Sra. Unsupervised robust nonparametric learning of hidden community properties Jul 2017.
    [.bib] [.pdf]
  2. Chulhee Yun, Suvrit Sra, Ali Jadbabaie. Global optimality conditions for deep neural networks. Jun 2017.
    [.bib] [arXiv]
    @Article{yuSrJa17,
    author = {Chulhee Yun and Suvrit Sra and Ali Jadbabaie},
    title = {Global optimality conditions for deep neural networks},
    journal = {arXiv:},
    year = {2017},
    note = {{\it Preprint}}
    }
    
  3. Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra. Distributional Adversarial Networks. 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}
    }             
    
  4. Hongyi Zhang, Suvrit Sra. First-order methods for Riemannian optimization. Jun 2017.
    [.bib] [arXiv]
    @Article{zhSra17,
    author = {Hongyi Zhang and Suvrit Sra},
    title = {First-order methods for Riemannian optimization},
    journal = {arXiv:},
    year = {2017},
    note = {{\it Preprint}}
    }
    
  5. Reshad Hosseini, Suvrit Sra. An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization. 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}}
    }
    
  6. Anoop Cherian, Suvrit Sra, Richard Hartley. Sequence Summarization Using Order-constrained Kernelized Feature Subspaces. May 2017.
    [.bib] [arXiv]
    @Article{chSrHa17,
    author = {Anoop Cherian and Suvrit Sra and Richard Hartley},
    title = {Sequence Summarization Using Order-constrained Kernelized Feature Subspaces},
    journal = {arXiv:1705.08583},
    year = {2017},
    note = {{\it Preprint}}
    }
    
  7. Zelda Mariet, Suvrit Sra. Elementary Symmetric Polynomials for Optimal Experimental Design. May 2017.
    [.bib] [arXiv]
  8. Zelda Mariet, John Holodnak, Jason Matterer, Suvrit Sra. Labels as Features for Cluster-Based Classifier Evaluation. Feb 2017.
    [.bib] [arXiv]
  9. Chengtao Li, Suvrit Sra, Stefanie Jegelka. Markov Chains for Cardinality Restricted Strongly Rayleigh Measures via Chain Combination Feb 2017.
  10. Chengtao Li, Stefanie Jegelka, Suvrit Sra Polynomial Time Dual Volume Sampling. Feb 2017.
    [.bib] [arXiv]
  11. Suvrit Sra. Directional Statistics in Machine Learning: a Brief Review. Jan 2016.
    [.bib] [.pdf]
  12. Suvrit Sra. Inequalities via symmetric polynomial majorization. Sep 2016.
    [.bib] [arXiv] [.pdf]
    @Article{sra15esym,
    author = {Suvrit Sra},
    title = {Inequalities via symmetric polynomial majorization},
    journal = {arXiv:1509.05902},
    year = {2015},
    note = {{\it Preprint}}
    }
    
  13. K.S. Sesh Kumar, Álvaro J. Barbero, Suvrit Sra, Stefanie Jegelka, and Francis Bach. Convex Optimization for Parallel Energy Minimization.
    [.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},
    note = {{\it Preprint}}
    }
    
  14. Álvaro J. Barbero, Suvrit Sra. Modular proximal optimization with application to total variation regularization. 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}
    }
    
  15. 2017

  16. Ke Jiang, Suvrit Sra, Brian Kulis. Combinatorial Topic Models using Small–Variance Asymptotics. Artificial Intelligence and Statistics (AISTATS) 2017
    [.bib] [arXiv]
  17. 2016

  18. Zelda Mariet, Suvrit Sra Kronecker Determinantal Point Processes. Advances in Neural Information Processing Systems (NIPS) 2016.
    [.bib] [arXiv]
  19. Sashank Reddi, Suvrit Sra, Barnabas Poczos, Alex Smola. Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum OptimizationAdvances in Neural Information Processing Systems (NIPS) 2016.
    [.bib] [.pdf] [arXiv]
  20. Hongyi Zhang, Sashank Reddi, Suvrit Sra Fast stochastic optimization on Riemannian manifolds. Advances in Neural Information Processing Systems (NIPS) 2016. May 2016
    [.bib] [arXiv]
  21. Chengtao Li, Stefanie Jegelka, Suvrit Sra. Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling Advances in Neural Information Processing Systems (NIPS) 2016.
    [.bib] [arXiv]
  22. Sashank Reddi, Suvrit Sra, Barnabas Poczos, Alex Smola. Stochastic Frank-Wolfe Methods for Nonconvex Optimization. to appear in the 54th Annual Allerton Conference on Communication, Control, and Computing. 2016
    [.bib] [.pdf] [arXiv]
  23. Anoop Cherian, Suvrit Sra. Riemannian dictionary learning and sparse coding for positive definite matrices. To appear in 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},
    }
    
  24. Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alexander J. Smola. Fast incremental method for smooth nonconvex optimization. To appear in IEEE Conference on Decision and Control (CDC), Dec 2016.
    [.bib] [arXiv]
  25. Anoop Cherian, Suvrit Sra. Positive Definite Matrices: Data Representation and Applications to Computer Vision. Book chapter, to appear in Algorithmic Advances in Riemannian Geometry and Applications, Springer, 2016.
    [.bib] [.pdf]
  26. Suvrit Sra, Reshad Hosseini. Geometric Optimization in Machine Learning. Book chapter, to appear in Algorithmic Advances in Riemannian Geometry and Applications, Springer, 2016.
    [.bib] [.pdf]
  27. Hongyi Zhang, Suvrit Sra. First-order methods for geodesically convex optimization. Conference on Learning Theory (COLT 2016)
    [.bib] [TBA] [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}}
    }
    
  28. Sashank Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alexander J. Smola. Stochastic variance reduction for nonconvex optimization. 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}}
    }
    
  29. Pourya H. Zadeh, Reshad Hosseini, Suvrit Sra. Geometric Mean Metric Learning. International Conference on Machine Learning (ICML 2016)
    [.bib] [.pdf]
  30. Chengtao Li, Stefanie Jegelka, Suvrit Sra. Fast DPP Sampling for Nyström with Application to Kernel Methods. International Conference on Machine Learning (ICML 2016)
    [.bib] [.pdf] [arXiv]
  31. Chengtao Li, Suvrit Sra, Stefanie Jegelka. Gaussian quadrature for matrix inverse forms with applications. International Conference on Machine Learning (ICML 2016)
    [.bib] [.pdf] [arXiv]
  32. Y.-X. Wang, V. Sadhanala, W. Dai, W. Neiswanger, Suvrit Sra, E. P. Xing. Asynchronous Parallel Block-Coordinate Frank-Wolfe. International Conference on Machine Learning (ICML 2016)
    [.bib] [TBA] [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},
    }
    
  33. Suvrit Sra. On the Matrix Square Root via Geometric Optimization. Electronic Journal of Linear Algebra (ELA) (May 2016).
    [.bib] [arXiv] [TBA]
    @Article{sraRoot,
    author = {Suvrit Sra},
    title = {{On the matrix square root and geometric optimization}},
    journal = {arXiv:1507.08366},
    year = {2015},
    note = {\it Preprint}
    }
    
  34. Justin Solomon, Gabriel Peyré, Vladimir Kim, Suvrit Sra. Entropic Metric Alignment for Correspondence Problems. 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},
    }
    
  35. Reshad Hosseini, Suvrit Sra, Lucas Theis, Matthias Bethge. Inference and mixture modelling with the Elliptical Gamma Distribution. 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}},
    }
    
  36. Zelda Mariet, Suvrit Sra. Diversity Networks. 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}
    }
    
  37. Lev Borisov, Patrizio Neff, Suvrit Sra, Christian Thiel. The sum of squared logarithms inequality in arbitrary dimensions. Linear Algebra and its Applications (LAA); accepted 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}
    }
    
  38. Chengtao Li, Stefanie Jegelka, Suvrit Sra. Efficient sampling for k-determinantal point processes. 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},
    }
    
  39. Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola. AdaDelay: Delay sensitive distributed stochastic convex optimization. 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}
    }
    
  40. 2015

  41. Suvrit Sra. Positive Definite Matrices and the S-Divergence. 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}
    }
    
  42. Reshad Hosseini, Suvrit Sra. Matrix Manifold Optimization for Gaussian Mixtures. 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}
    }
    
  43. Sashank Reddy, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alexander J. Smola. Asynchronous variance reduced stochastic gradient descent. 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},
    }
    
  44. Suvrit Sra. On inequalities for normalized Schur functions. 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,
    }
    
  45. Minghua Lin, Suvrit Sra. A proof of Thompson's determinantal inequality. 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},
    note = {to appear}
    }
    
  46. Wolfgang Berndt, Suvrit Sra. Hlawka-Popoviciu inequalities on positive definite tensors. 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}
    }
    
  47. Sashank J. Reddi, Ahmed Hefny, Carlton Downey, Abhinava Dubey, Suvrit Sra. Large-scale randomized-coordinate descent methods with non-separable linear constraints.. 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},
    }
    
  48. Zelda Mariet, Suvrit Sra. Fixed-point algorithms for learning determinantal point processes. 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},
    }
    
  49. Suvrit Sra, Reshad Hosseini. Conic geometric optimisation on the manifold of positive definite matrices. 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},
    }
    
  50. Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge. Data Modeling with the Elliptical Gamma Distribution. 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,
    }
    
  51. 2014

  52. Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra. Efficient Structured Matrix Rank Minimization. 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}
    }
    
  53. Anoop Cherian, Suvrit Sra. Riemannian sparse coding for positive definite matrices. 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}
    }
    
  54. Matt Wytock, Suvrit Sra, Zico Kolter. Fast Newton methods for the group fused lasso. 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}
    }
    
  55. Anoop Cherian, Suvrit Sra, Vassilios Morellas, and Nikos Papanikolopoulos. Efficient nearest neighbors via robust sparse hashing. 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}
    }
    
  56. David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, and Bernhard Schölkopf. Randomized Nonlinear Component Analysis. 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}
    }
    
  57. Samaneh Azadi and Suvrit Sra. Towards stochastic alternating direction method of multipliers. 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}
    }
    
  58. Suvrit Sra. Nonconvex proximal splitting: batch and incremental algorithms. 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},
    }
    
  59. 2013

  60. Suvrit Sra. Tractable large-scale optimization in machine learning. 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},
    }
    
  61. Suvrit Sra and Reshad Hosseini. Geometric optimisation on positive definite matrices with application to elliptically contoured distributions. 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,
    }
    
  62. Stefanie Jegelka, Francis Bach, and Suvrit Sra. Reflection methods for user-friendly submodular optimization. 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}
    }
    
  63. Carlos M. Alaiz, Francesco Dinuzzo, and Suvrit Sra. Correlation matrix nearness and completion under observation uncertainty. 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},
    }
    
  64. 2012

  65. Anoop Cherian, Suvrit Sra, Arindam Banerjee, and Nikos Papanikolopoulos. Jensen-Bregman LogDet Divergence for Efficient Similarity Computations on Positive Definite Tensors. 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},
    }
    
  66. Suvrit Sra. A new metric on the manifold of kernel matrices with application to matrix geometric means. 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}
    }
    
  67. Suvrit Sra. Scalable nonconvex inexact proximal splitting. Advances of Neural Information Processing Systems (NIPS 2012)
    [.bib] [.pdf]
  68. Suvrit Sra, Dmitrii B. Karp. The multivariate Watson distribution: Maximum-likelihood estimation and other aspects. Journal of Multivariate Analysis (accepted 2012)
    [.bib] [arXiv] [.pdf] [.pdf]
  69. Suvrit Sra. Explicit eigenvalues of certain scaled trigonometric matrices. Linear Algebra and its Applications (accepted Jul 2012)
    [.bib] [arXiv] [.pdf]
  70. Suvrit Sra. Fast projection onto mixed-norm balls with applications. Data Minining and Knowledge Discovery. 2012
    [.bib] [arXiv] [.pdf]
  71. 2011

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

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

  92. Michael Hirsch, Suvrit Sra, Bernhard Schölkopf and Stefan Harmeling. Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution. MPI Technical Report #188 Nov 2009
    [.bib] [.pdf]
  93. Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra Text Clustering with Mixture of von Mises-Fisher Distributions. In: "Text Mining: Theory, Applications, and Visualization" eds. A. N. Srivastava and M. Sahami, CRC Press. 2009.
    [.bib] [.pdf]
  94. Stefanie Jegelka, Suvrit Sra, Arindam Banerjee. Approximation Algorithms for Tensor clustering. Algorithmic Learning Theory (ALT 2009).
    [.bib] [arXiv]
  95. Stefan Harmeling, Michael Hirsch, Suvrit Sra, Bernhard Schölkopf. Online Blind Deconvolution for Astronomy. IEEE Interational Conferemce on Computational Photography (ICCP 2009)
    [.bib] [.pdf]
  96. Suvrit Sra, Dongmin Kim, Inderjit S. Dhillon, Bernhard Schölkopf. A new non-monotonic algorithm for PET image reconstruction. IEEE Nuclear Science Symposium / Medical Imaging Conf. (NSS/MIC 2009)
    [.bib] [Conference Record M03-2: ]
  97. Brian Kulis, Suvrit Sra, Inderjit S. Dhillon. Scalable Semidefinite Programming using Convex Perturbations. Artificial Intelligence and Statistics (AISTATS 2009)
    [.bib] [.pdf]
  98. 2008

  99. Suvrit Sra. Block-Iterative Algorithms for Non-negative Matrix Approximation. IEEE International Conference on Data Mining (ICDM 2008)
    [.bib] [.pdf]
  100. Justin Brickell, Inderjit S. Dhillon, Suvrit Sra, Joel A. Tropp. The Metric Nearness Problem. >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]
  101. Suvrit Sra, Stefanie Jegelka, and Arindam Banerjee. Approximation Algorithms for Bregman Clustering Co-clustering and Tensor Clustering. MPI Technical Report #177 2008.
    [.bib] [.pdf]
  102. Suvrit Sra. Block iterative algorithms for non-negative matrix approximation. MPI Technical Report #176 2008
    [.bib] [.pdf]
  103. Suvrit Sra, Dongmin Kim, and Bernhard Schölkopf. Non-monotonic Poisson Likelihood Maximization. MPI Technical Report #170. Jun 2008
    [.bib] [.pdf]
  104. Dongmin Kim, Suvrit Sra, and Inderjit S. Dhillon. A New Non-monotonic Gradient Projection Method for the Non-negative Least Squares Problem. Computer Sciences, University of Texas at Austin, TR-08-28.
    [.bib] [.pdf]
  105. Some links below are broken; under construction

    2007

  106. Matrix Nearness Problems in Data Mining. Suvrit Sra Ph.D. Thesis. University of Texas at Austin. Aug. 2007
    Thesis: .pdf [.bib] [arXiv] [.pdf]
  107. 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]
  108. 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]
  109. Fast Projection-Based Methods for the Least Squares Nonnegative Matrix Approximation Problem by Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon in Statistical Analysis and Data Mining vol. 1 pp. 38-51 (2007);
    [.pdf]; [author PDF] [.bib]
  110. 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]
  111. 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]
  112. 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]
  113. 2006--2003

  114. 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]
  115. Incremental Aspect Models for Mining Document Streams by Arun Surendran, Suvrit Sra in Principles and Practice of Knowledge Discovery in Databases (PKDD) 2006;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  116. Efficient Large Scale Linear Programming Support Vector Machines by Suvrit Sra in European Conference on Machine Learning (ECML) 2006.;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  117. Row-action Methods for Compressed Sensing by Suvrit Sra, Joel A. Tropp in International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2006.;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  118. 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]
  119. Generalized Nonnegative Matrix Approximations with Bregman Divergences by Inderjit S. Dhillon, Suvrit Sra in Advances Neural Information Processing Systems (NIPS) 2005.;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  120. Minimum Sum Squared Residue based Co-clustering of Gene Expression data by Hyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, Suvrit Sra in SIAM International Conference on Data Mining (SDM) 2004;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  121. Triangle Fixing Algorithms for the Metric Nearness Problem by Inderjit S. Dhillon, Suvrit Sra, J. A. Tropp in Advances in Neural Information Processing Systems (NIPS) 2004;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  122. Generative Model-Based Clustering of Directional Data by Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Suvrit Sra in International Conference on Knowledge Discovery and Data Mining (KDD) 2003.;
    Paper: [.pdf [.bib] [arXiv] [.pdf]
  123. 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]
  124. 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]
  125. 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]
  126. 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]
  127. 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]
  128. 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