Peer Reviewed Publications

I would like to thank NSF, ONR, MIT RSC and the Betty-Moore Sloan foundation for supporting my research group.


  • ‘‘ Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms ’’, Hussein Hazimeh and Rahul Mazumder; 2018 pdffile

    For code see Link

  • ‘‘ Computation of the Maximum Likelihood estimator in low-rank Factor Analysis ’’, Koulik Khamaru, Rahul Mazumder; 2018 ArXiv

  • ‘‘ Mining Events with Declassified Diplomatic Documents ’’, Yuanjun Gao, Jack Goetz, Rahul Mazumder, Matthew Connelly; 2017 ArXiv

  • ‘‘ Sparse principal component analysis and its l1-relaxation ’’, Santanu Dey, Rahul Mazumder, Marco Molinaro, Guanyi Wang; 2017 Opt-Online

  • ‘‘ Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low ’’, Rahul Mazumder, Peter Radchenko, Antoine Dedieu; 2017. ArXiv

  • ‘‘ The Trimmed Lasso: Sparsity and Robustness ’’, Dimitris Bertsimas, Martin Copenhaver and Rahul Mazumder; 2017. ArXiv

  • ‘‘ Matrix Completion with Nonconvex Regularization: Spectral Operators and Scalable Algorithms ’’, Rahul Mazumder, Diego F. Saldana, Haolei Weng; 2016.

  • ‘‘ Scalable Computation of Regularized Precision Matrices via Stochastic Optimization ’’, Yves F. Atchadé, Rahul Mazumder, Jie Chen; 2015. ArXiv

  • ‘‘AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods’’, Robert Freund, Paul Grigas and Rahul Mazumder; 2013, ArXiv (preliminary version accepted and presented at ROKS workshop, July 8-10, 2013, Belgium).

  • ‘‘A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso", Rahul Mazumder and Deepak Agarwal, 2011, ArXiv-link

Published/To Appear Methodology Papers

  • ‘‘Flexible Low-Rank Statistical Modeling with Side Information’’, Will Fithian and Rahul Mazumder; 2018+, Statistical Science (to appear) , ArXiv

    [previously circulated as: ``Scalable Convex Methods for Flexible Low-Rank Matrix Modeling"]
  • ‘‘ A Computational Framework for Multivariate Convex Regression and its Variants ’’, Rahul Mazumder, Arkopal Choudhury, Garud Iyengar, Bodhisattva Sen; 2017+ Journal of the American Statistical Association, Theory and Methods (to appear), ArXiv

  • ‘‘ Certifiably Optimal Low Rank Factor Analysis ’’, Dimitris Bertsimas, Martin Copenhaver, Rahul Mazumder; 2017. Journal of Machine Learning Research 18(29):1-53 JournalLink

  • ‘‘ The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization ’’, Rahul Mazumder, Peter Radchenko; 2017 IEEE Transactions on Information Theory 63 (5), 3053-3075 ArXiv

  • ‘‘ An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion ’’, Robert M. Freund, Paul Grigas, Rahul Mazumder; 2017 SIAM Journal on Optimization 27(1), 319-346 ArXiv

  • ‘‘A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives ’’, Robert M. Freund, Paul Grigas, Rahul Mazumder; 2017 Annals of Statistics (to appear) pdffile

  • ‘‘ Best Subset Selection via a Modern Optimization Lens ’’, Dimitris Bertsimas, Angela King and Rahul Mazumder; 2016 Annals of Statistics ArXiv

  • ‘‘ Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares’’, Trevor Hastie, Rahul Mazumder, Jason Lee, Reza Zadeh; 2015 Journal of Machine Learning Research ArXiv

  • ‘‘Least Quantile of Squares Regression via Modern Optimization’’, Dimitris Bertsimas and Rahul Mazumder; 2014, Annals of Statistics ArXiv

  • ‘‘The Graphical Lasso : New Insights and Alternatives", Rahul Mazumder and Trevor Hastie, 2012, Electronic Journal of Statistics 6, 2125-2149 link

  • ‘‘Exact covariance thresholding into connected components for large-scale Graphical Lasso", 2012, Rahul Mazumder and Trevor Hastie, Journal of Machine Learning Research 13, 781-794. journal link Older ArXiv version (2011) available at ArXiv-link

  • ‘‘Spectral Regularization Algorithms for Learning Large Incomplete Matrices", Rahul Mazumder, Trevor Hastie and Rob Tibshirani, 2010, Journal of Machine Learning Research 11, 2287-2322. pdf

  • ‘‘ SparseNet: Coordinate Descent with Non-Convex Penalties" Rahul Mazumder, Jerome Friedman and Trevor Hastie, 2011, Journal of American Statistical Association, Theory and Methods, 106(495):1125 - 1138. pdf

  • ‘‘Modeling Item-Item Similarities for Personalized Recommendations on Yahoo! Front Page," Deepak Agarwal, Liang Zhang and Rahul Mazumder, 2011, Annals of Applied Statistics, 5(3): 1839-1875. link

  • ‘‘Projected likelihood contrasts for testing homogeneity in finite mixture models with nuisance parameters", 2008, Debapriya Sengupta and Rahul Mazumder, Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen (Beachwood, Ohio, USA: Institute of Mathematical Statistics), 272-281.

Published Application Papers

  • ‘‘Turbulence, suspension and downstream fining due to flow over a sand-gravel mixture bed", Koeli Ghoshal, Rahul Mazumder, Chandan Chakraborty and Bijoy. S. Mazumder, 2013, International Journal of Sediment Research , 28(2).

  • ‘‘Assessing the significance of global and local correlations under spatial autocorrelation: a nonparametric approach", 2013, Julia Validomat, Rahul Mazumder, Alex McInturff, Douglas McCauley and Trevor Hastie, 2013, Biometrics, link

  • ‘‘Non-Negative Matrix Completion for Bandwidth Extension: A Convex Optimization Approach’’, Dennis Sun and Rahul Mazumder; 2013, IEEE Machine Learning for Signal Processing Code

  • ‘‘Fluid flow pattern analysis in a trough region: a nonparametric approach", Rahul Mazumder, 2008, Journal of Applied Statistics, 35(6) .

  • ‘‘Clustering based on geometry and interactions of turbulence bursting rate processes in a trough region", Rahul Mazumder, 2007, Environmetrics 18(4).

  • ‘‘ Statistical characterization of circulation patterns and direction of turbulent flow over a waveform structure", 2006, Rahul Mazumder and B. S. Mazumder, Environmetrics, 17(5).


  • PhD Thesis:

‘‘ Topics in Sparse Multivariate Statistics", Stanford University (Dept. of Statistics).

  • Masters’ Thesis:

‘‘Local scale-space contrasts via Gaussian Mixture Ensembles for speech signal segmentation", Indian Statistical Institute, 2007.