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Key: πŸ’ Machine learning, Statistics, Optimization;    πŸ€ Math
Shortcuts: [2015]; [2014]; [up to 2013];

Preprints, Submitted Articles, Upcoming manuscripts

  1. Inequalities via elementary symmetric polynomial monotonicity   πŸ€
          Suvrit Sra
         (Version: Sep 2015)
         
    @Article{sra15esym,
    author = {Suvrit Sra},
    title = {Inequalities via elementary symmetric polynomial},
    journal = {arXiv:1509.xxxx},
    year = {2015},
    note = {\it Preprint}
    }
    
  2. Machine learning with positive definite matrices (upcoming) πŸ’
          Anoop Cherian, Suvrit Sra

  3. Directional statistics in machine learning (upcoming) πŸ’
          Suvrit Sra

  4. Aspects of geometric optimization in machine learning (upcoming) πŸ’
          Suvrit Sra, Reshad Hosseini

  5. Efficient sampling for k-determinantal point processes πŸ’
          Chengtao Li, Stefanie Jegelka, Suvrit Sra
         (Version: Sep 2015)
         
    @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},
    }
    

  6. The sum of squared logarithms inequality in arbitrary dimensions   πŸ€
          Lev Borisov, Patrizio Neff, Suvrit Sra, and Christian Thiel
         (Version: Aug 2015)
         
    @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}
    }
    

  7. On the matrix square root and geometric optimization πŸ’ πŸ€
          Suvrit Sra
         (Version: Jul 2015)
         
    @Article{sraRoot,
    author = {Suvrit Sra},
    title = {{On the matrix square root and geometric optimization}},
    journal = {arXiv:1507.08366},
    year = {2015},
    note = {\it Preprint}
    }
    

  8. Riemannian dictionary learning and sparse coding for positive definite matrices πŸ’
          Anoop Cherian, Suvrit Sra
         (Version of: July 2015)
         
    @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},
    }
    

  9. Inference and mixture modelling with the Elliptical Gamma Distribution πŸ’
          Reshad Hosseini, Suvrit Sra, Lucas Theis, Matthias Bethge
         (Submitted: Oct 2014; Revised: Jul. 2015)
         
    @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 Submitted}},
    }
    

  10. AdaDelay: Delay sensitive distributed stochastic convex optimization πŸ’
          Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola
         (Version: Jun 2015)
         
    @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}
    }
    

  11. Convex Optimization for Parallel Energy Minimization πŸ’
          K.S. Sesh Kumar, Álvaro J. Barbero, Suvrit Sra, Stefanie Jegelka, and Francis Bach
         
    @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}
    }
    

  12. Modular proximal optimization with application to total variation regularization" πŸ’
          Γlvaro J. Barbero, Suvrit Sra
         (Submitted: Nov 2013; Revised: Oct. 2014)
         
    @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. Asynchronous Parallel Block-Coordinate Frank-Wolfe πŸ’
          Y.-X. Wang, V. Sadhanala, W. Dai, W. Neiswanger, Suvrit Sra, E. P. Xing
         
    @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},
    }
    

  14. 2015

  15. Positive Definite Matrices and the S-Divergence πŸ€
          Suvrit Sra
         Proceedings of the American Mathematical Society (to appear)
         
    @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}
    }
    


  16. Manifold optimization for mixture modeling πŸ’
          Reshad Hosseini, Suvrit Sra
         Advances in Neural Information Processing Systems (NIPS 2015) (to appear)
         
    @Article{hosSra15b,
    author = {Reshad Hosseini and Suvrit Sra},
    title = {{Manifold optimization for mixture modeling}},
    journal = {arXiv:1506.07677},
    year = {2015},
    note = {\it Submitted}
    }
    

  17. Asynchronous variance reduced stochastic gradient descent πŸ’
          Sashank Reddy, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alexander J. Smola
         Advances in Neural Information Processing Systems (NIPS 2015) (to appear)
         
    @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},
    }
    

  18. On inequalities for normalized Schur functions πŸ€
          Suvrit Sra
          European Journal of Combinatorics    (Submit: Feb 2015, Accept: May 2015)
         
    @Article{sra15a,
    author = {Suvrit Sra},
    title = {{On inequalities for normalized Schur functions}},
    journal = {European J. Combinatorics},
    year = {2015},
    }
    

  19. A proof of Thompson's determinantal inequality πŸ€
          Minghua Lin, Suvrit Sra
          Mathematical Notes (Accept: Jun 2015)
         
    @Article{linSra14,
    author = {Minghua Lin and Suvrit Sra},
    title = {{Complete strong superadditivity of generalized matrix functions}},
    journal = {Mathematical Notes},
    year = {2015},
    note = {to appear}
    }
    

  20. Hlawka-Popoviciu inequalities on positive definite tensors πŸ€
          Wolfgang Berndt, Suvrit Sra
          Linear Algebra and its Applications    (Accept: Feb 2015)
         
    @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}
    }
    

  21. Large-scale randomized-coordinate descent methods with non-separable linear constraints πŸ’
          Sashank J. Reddi, Ahmed Hefny, Carlton Downey, Abhinava Dubey, Suvrit Sra
          Uncertainty in Artificial Intelligence (UAI 2015)    (Accept: Oct 2014))
         
    @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},
    }
    

  22. Fixed-point algorithms for learning determinantal point processes πŸ’
          Zelda Mariet, Suvrit Sra
          International Conf. on Machine Learning (ICML 2015);
         
    @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},
    }
    

  23. Conic geometric optimisation on the manifold of positive definite matrices πŸ€ πŸ’
          Suvrit Sra, Reshad Hosseini
          SIAM J. Optimization (SIOPT)   (Jan 2015)
         
    @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},
    note = {Accepted}
    }
    

  24. Data Modeling with the Elliptical Gamma Distribution πŸ’
          Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge
          Artificial Intelligence and Statistics (AISTATS 2015)
         
    @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,
    }
    


  25. 2014

  26. Efficient Structured Matrix Rank Minimization πŸ’
          Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra
          Advances in Neural Information Processing Systems (NIPS)
         
    @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}
    }
    

  27. Riemannian sparse coding for positive definite matrices πŸ’
          Anoop Cherian, Suvrit Sra
          European Conference on Computer Vision (ECCV)
         
    @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}
    }
    

  28. Fast Newton methods for the group fused lasso πŸ’
          Matt Wytock, Suvrit Sra, Zico Kolter,
          Uncertainty in Artificial Intelligence (UAI)
         
    @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}
    }
    

  29. Efficient nearest neighbors via robust sparse hashing πŸ’
          Anoop Cherian, Suvrit Sra, Vassilios Morellas, and Nikos Papanikolopoulos
          IEEE Transactions on Image Processing
         
    @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}
    }
    

  30. Randomized Nonlinear Component Analysis πŸ’
          David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, and Bernhard SchΓΆlkopf
          International Conf. on Machine Learning (ICML'14);
         
    @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}
    }
    
  31. Towards stochastic alternating direction method of multipliers πŸ’
          Samaneh Azadi and Suvrit Sra
          International Conf. on Machine Learning (ICML'14);
         
    @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}
    }
    

  32. Nonconvex proximal splitting: batch and incremental algorithms πŸ’
          Suvrit Sra
    Invited book chapter in:
          Regularization, Optimization, Kernels, and Support Vector Machines
          (Editors: J. A.K. Suykens, M. Signoretto, A. Argyriou. (Mar 2014);
         
    @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},
    }
    

  33. 2013

  34. Tractable large-scale optimization in machine learning πŸ’
          Suvrit Sra
    Invited book chapter in:
          Tractability Practical Approaches to Hard Problems
          (Editors: L. Bordeaux, Y. Hamadi, P. Kohli); Aug 2013.
         
    @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},
    }
    

  35. Geometric optimisation on positive definite matrices with application to elliptically contoured distributions πŸ’
          Suvrit Sra and Reshad Hosseini
          Advances in Neural Information Processing Systems (NIPS)
         
    @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,
    }
    

  36. Reflection methods for user-friendly submodular optimization πŸ’
          Stefanie Jegelka, Francis Bach, and Suvrit Sra
          Advances in Neural Information Processing Systems (NIPS)
         
    @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}
    }
    

  37. Correlation matrix nearness and completion under observation uncertainty πŸ’
          Carlos M. Alaiz, Francesco Dinuzzo, and Suvrit Sra
          IMA Journal of Numerical Analysis
         
    @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},
    }
    

  38. 2012

  39. Jensen-Bregman LogDet Divergence for Efficient Similarity Computations on Positive Definite TensorsπŸ’
          Anoop Cherian, Suvrit Sra, Arindam Banerjee, and Nikos Papanikolopoulos
          IEEE Tran. Pattern. Analy. Mach. Intell. (TPAMI) Dec. 2012;
         
    @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},
    }
    

  40. A new metric on the manifold of kernel matrices with application to matrix geometric means πŸ’πŸ€
          Suvrit Sra
          Advances of Neural Information Processing Systems (NIPS) 2012;
         
    @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}
    }
    

  41. Scalable nonconvex inexact proximal splitting πŸ’
          Suvrit Sra
          Advances of Neural Information Processing Systems (NIPS) 2012;
         
         [.pdf]
  42. The multivariate Watson distribution: Maximum-likelihood estimation and other aspects πŸ’ πŸ€
          Suvrit Sra and Dmitrii B. Karp
          Journal of Multivariate Analysis (submitted Apr. 2011; accepted Aug 2012);
         
         Preprint: [stat.CO-1104.4422]; official version: [.pdf]
  43. Explicit eigenvalues of certain scaled trigonometric matrices πŸ€
          Suvrit Sra
          Linear Algebra and its Applications
         
         (Submitted Jan., 2012, accepted Jul 2012)
         [.pdf]; preprint: [math.NA-1201.4651]
  44. Fast projection onto mixed-norm balls with applications πŸ’
          Suvrit Sra
          Data Minining and Knowledge Discovery (DMKD) 2012;
         
         preprint: [stat.ML-1204.1437]
  45. 2011

  46. A non-monotonic method for large-scale non-negative least squares
          Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
          (submitted: Nov. 2010; revised: May. 2011; accepted: Dec. 2011)
          Optimization Methods and Software;
         
         Paper: [.pdf]
  47. 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); πŸ’
         
         Paper: [.pdf]; Bugfix version: [.pdf];
  48. 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); πŸ’
         
         Paper: [.pdf]
  49. Fast projections onto L1,q-norm balls for grouped feature selection
          Suvrit Sra
          European Conf. on Machine Learning (ECML) (2011); πŸ’      (Best Paper Runner up Award)
         
         Paper: [.pdf]
  50. Fast Newton-type Methods for Total-Variation with Applications
          Γlvaro J. Barbero, Suvrit Sra
          International Conference on Machine Learning (ICML) June, 2011; πŸ’
         
         Paper: [.pdf]
  51. A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of $I_s(x)$
          Suvrit Sra
          Computational Statistics (2011); πŸ’
         
         Preprint: [.pdf]
  52. Optimization for Machine Learning
          Suvrit Sra, Sebastian Nowozin, Stephen J. Wright
          MIT Press (2011); πŸ’
          (Book at machine learning and optimization users, students, researchers.)
          Buy it at: MIT Press or Amazon or Barnes and Noble

  53. Projected Newton-type methods in machine learning
          Mark Schmidt, Dongmin Kim, Suvrit Sra
          Chapter in: Optimization for Machine Learning. MIT Press (2011); πŸ’
         
         Preprint: [.pdf]
  54. Online Multi-frame Blind Deconvolution with Super-resolution and Saturation Correction
          Michael Hirsch, Stefan Harmeling, Suvrit Sra, Bernhard SchΓΆlkopf
          Astronomy & Astrophysics Feb. (2011);
         
         Paper: [.pdf]
  55. Denoising sparse noise via online dictionary learning
          Anoop Cherian, Suvrit Sra, Nikos Papanikolopoulos
          IEEE Conf. Speech Acoustics and Signal Processing (ICASSP), May 2011;
         
          Paper: [.pdf]
  56. 2010

  57. Sparse inverse covariance estimation using an adaptive gradient method
          Suvrit Sra and Dongmin Kim
          Preprint only: of Jun. 2010; πŸ’
         
         Preprint [.pdf]
  58. 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;
         
         Paper: [.pdf]
  59. 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; πŸ’
         
         Paper: [.pdf]
  60. Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction via Incremental EM
          Stefan Harmeling, Suvrit Sra, Michael Hirsch, Bernhard SchΓΆlkopf
          IEEE International Conference on Image Processing (ICIP). 2010;
         
         Paper: [.pdf]
  61. Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution
          Michael Hirsch, Suvrit Sra, Bernhard SchΓΆlkopf, Stefan Harmeling
          IEEE Conf. Computer Vision & Pattern Recognition (CVPR 2010);
         
         Paper: [.pdf]
  62. Sparse nonnegative matrix approximation: new formulations and algorithms.
          Rashish Tandon and Suvrit Sra.
          MPI Technical Report #193. Sep. 2010; πŸ’
         
         Paper: [.pdf]
  63. Fast algorithms for total-variation based optimization
          Alvaro J. Barbero and Suvrit Sra.
          MPI Technical Report #194. Aug. 2010; πŸ’
         
         Paper: [.pdf]
  64. Generalized proximity and projection with norms and mixed-norms
          Suvrit Sra
          MPI Technical Report #192. May 2010; πŸ’
         
         Paper: [.pdf]
  65. 2009

  66. Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution
          Michael Hirsch, Suvrit Sra, Bernhard SchΓΆlkopf and Stefan Harmeling
          MPI Technical Report #188, Nov. 2009;
         
         Paper: [.pdf]
  67. Text Clustering with Mixture of von Mises-Fisher Distributions
          Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, and Suvrit Sra
          Text Mining: Theory, Applications, and Visualization
          Book edited by: A. N. Srivastava and M. Sahami. CRC Press (2009); πŸ’
         
         (Invited chapter)
         Preprint: [.pdf]
  68. Approximation Algorithms for Tensor clustering
          Stefanie Jegelka, Suvrit Sra, Arindam Banerjee
          Algorithmic Learning Theory (ALT) 2009.; πŸ’
         
          Extended version [cs.DS/0812.0389]
  69. Online Blind Deconvolution for Astronomy
          Stefan Harmeling, Michael Hirsch, Suvrit Sra, Bernhard SchΓΆlkopf
          IEEE Interational Conferemce on Computational Photography (ICCP). 2009;
         
         Paper: [.pdf]
  70. A new non-monotonic algorithm for PET image reconstruction
          Suvrit Sra, Dongmin Kim, Inderjit S. Dhillon, Bernhard SchΓΆlkopf
          IEEE Nuclear Science Symposium / Medical Imaging Conf. (NSS/MIC). 2009;
         
          Conference Record M03-2: [.pdf]
  71. Scalable Semidefinite Programming using Convex Perturbations
          Brian Kulis, Suvrit Sra, Inderjit S. Dhillon
          Artificial Intelligence and Statistics (AISTATS) 2009; πŸ’
         
         Paper: [.pdf]
  72. 2008

  73. Block-Iterative Algorithms for Non-negative Matrix Approximation
          Suvrit Sra
          IEEE International Conference on Data Mining (ICDM) 2008; πŸ’
         
         Paper: [.pdf]
  74. The Metric Nearness Problem
          Justin Brickell, Inderjit S. Dhillon, Suvrit Sra, Joel A. Tropp
          SIAM J. Matrix Analysis and Applications vol. 30 no. 1 pp. 375-396 (2008)
          SIAM Outstanding Paper Prize 2011 See: SIAM Website
          (one of three papers selected out of all papers published in SIAM Journals
          in the three years 2008--2010
    ); πŸ€
         
         Paper: [.pdf]; Preprint: [.pdf]
  75. Approximation Algorithms for Bregman Clustering Co-clustering and Tensor Clustering
          Suvrit Sra, Stefanie Jegelka, and Arindam Banerjee
          MPI Technical Report #177 2008; πŸ’
         
         Paper: [.pdf]
  76. Block iterative algorithms for non-negative matrix approximation
          Suvrit Sra
          MPI Technical Report #176 2008; πŸ’
         
         Paper: [.pdf]
  77. Non-monotonic Poisson Likelihood Maximization
          Suvrit Sra, Dongmin Kim, and Bernhard SchΓΆlkopf;
         
         MPI Technical Report #170, Jun. 2008
         Paper: [.pdf]
  78. 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. Jun. 2008;
         
         Paper: [-NA-]
  79. 2007

  80. Matrix Nearness Problems in Data Mining
          Suvrit Sra
          Ph.D. Thesis. University of Texas at Austin. Aug. 2007; πŸ’ πŸ€
         
         Thesis: [.pdf]
  81. Information-theoretic Metric Learning
          Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon
          International Conference on Machine Learning (ICML) 2007.; πŸ’
         
          (Best Student Paper)
         Paper: [.pdf]
  82. 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; πŸ’
         
         Recognized within best of SDM 2007 papers
         Paper: [.pdf]
  83. 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); πŸ’
         
         (Invited Paper)
         Paper: [.pdf]
  84. 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; πŸ’
         
         Paper: [.pdf]
  85. 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; πŸ’
         
          Paper: [.pdf]
  86. 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; πŸ’
         
         Paper: [.pdf]
  87. 2006--2003

  88. 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; πŸ’
         
         Paper: [.pdf]
  89. Incremental Aspect Models for Mining Document Streams
          by Arun Surendran, Suvrit Sra
          in Principles and Practice of Knowledge Discovery in Databases (PKDD) 2006; πŸ’
         
         Paper: [.pdf]
  90. Efficient Large Scale Linear Programming Support Vector Machines
          by Suvrit Sra
          in European Conference on Machine Learning (ECML) 2006.; πŸ’
         
         Paper: [.pdf]
  91. 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]
  92. 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); πŸ’
         
         Paper: [.pdf]
  93. Generalized Nonnegative Matrix Approximations with Bregman Divergences
          by Inderjit S. Dhillon, Suvrit Sra
          in Advances Neural Information Processing Systems (NIPS) 2005.; πŸ’
         
         Paper: [.pdf]
  94. 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; πŸ’
         
         Paper: [.pdf]
  95. 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]
  96. 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.; πŸ’
         
         Paper: [.pdf]
  97. Nonnegative Matrix Approximation: Algorithms and Applications
          Suvrit Sra and Inderjit S. Dhillon
         Comp. Sci., Univ. of Texas at Austin TR-06-27, Jun 2006; πŸ’
         
         Paper: [.pdf]
  98. 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; πŸ’
         
         Paper: [.pdf]
  99. 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]
  100. 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; πŸ€
         
         Paper: [.ps.gz]
  101. 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; πŸ’
         
         Paper: [.ps.gz]
  102. Modeling Data using Directional Distributions
          Inderjit S. Dhillon and Suvrit Sra
         Comp. Sci., Univ. of Texas at Austin TR-03-06, Jan. 2003; πŸ’
         
         Paper: [.ps.gz]

Misc. Manuscripts and Tidbits

  1. Explicit diagonalization of an anti-triangular stochastic matrix   πŸ€ πŸ’
         
         Suvrit Sra
           [.bib];     [Link: 1411.4107v2]; (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},
    }
    


  2. "A trivial remark on Cauchy-Schwarz" (Feb 2014); [.pdf]      
  3. ILAS Image #47. Ex.[#1]
  4. ILAS Image #48. Ex.[#2]
  5. ILAS Image #49. Ex.[#3]
  6. ILAS Image #51. Ex.[#4]