Preprints

  1. Sashank Reddi, Manzil Zaheer, Suvrit Sra, Francis Bach, Barnabas Poczos, Ruslan Salakhutdinov, Alexander Smola. A Generic Approach for Escaping Saddle points. May 2017.
    [.bib] [arXiv]
  2. Mikhail A. Langovoy, Akhilesh Gotmare, Martin Jaggi, Suvrit Sra. Unsupervised robust nonparametric learning of hidden community properties Jul 2017.
    [.bib] [.pdf]
  3. 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}}
    }
    
  4. 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}
    }             
    
  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, John Holodnak, Jason Matterer, Suvrit Sra. Labels as Features for Cluster-Based Classifier Evaluation. Feb 2017.
    [.bib] [arXiv]
  8. Chengtao Li, Suvrit Sra, Stefanie Jegelka. Markov Chains for Cardinality Restricted Strongly Rayleigh Measures via Chain Combination Feb 2017.
  9. Suvrit Sra. Directional Statistics in Machine Learning: a Brief Review. Jan 2016.
    [.bib] [.pdf]
  10. 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}}
    }
    
  11. 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}
    }
    
  12. 2017

  13. Suvrit Sra. Inequalities via symmetric polynomial majorization. Proceedings American Mathematical Society (PAMS) (accepted with minor revision) 2017.
    [.bib] [arXiv] [.pdf]
    @Article{sra15esym,
    author = {Suvrit Sra},
    title = {Inequalities via symmetric polynomial majorization},
    journal = {arXiv:1509.05902},
    year = {2015},
    note = {{\it Preprint}}
    }
    
  14. Zelda Mariet, Suvrit Sra. Elementary Symmetric Polynomials for Optimal Experimental Design. Advances in Neural Information Processing Systems (NIPS) 2017.
    [.bib] [arXiv]
  15. Chengtao Li, Stefanie Jegelka, Suvrit Sra Polynomial Time Dual Volume Sampling. Advances in Neural Information Processing Systems (NIPS) 2017.
    [.bib] [arXiv]
  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