Peer Reviewed Publications

    I would like to thank NSF, ONR, IBM, Liberty Mutual Insurance and MIT RSC for supporting my research group. In the past our research was funded by the Betty-Moore Sloan foundation.

    For a more complete list of publications, please see: Google Scholar

    Selected Preprints/Submitted Papers

    • Wenyu Chen and Rahul Mazumder (2020); ‘‘Multivariate Convex Regression at Scale ’’ [ArXiv]
         - Code

    • Hussein Hazimeh, Rahul Mazumder and Ali Saab (2020); ‘‘Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization ’’ [ArXiv]
         Received the ORC best student paper award 2020
         - Code

    • Antoine Dedieu, Hussein Hazimeh and Rahul Mazumder (2020); ‘‘Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives ’’ [ArXiv]
         - Code

    • Rahul Mazumder, Stephen Wright and Andrew Zheng (2019); ‘‘Computing Estimators of Dantzig Selector type via Column and Constraint Generation ’’ [ArXiv]
         - Code

    • Antoine Dedieu and Rahul Mazumder (2019); ‘‘Solving large-scale L1-regularized SVMs and cousins: the surprising effectiveness of column and constraint generation ’’ [ArXiv]
         - Code

    • Santanu Dey, Rahul Mazumder and Guanyi Wang (2018); ‘‘A convex integer programming approach for optimal sparse PCA ’’ [ArXiv]

    • Robert Freund, Paul Grigas and Rahul Mazumder (2018); ‘‘Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods ’’ [ArXiv]

    • Santanu Dey, Rahul Mazumder, Marco Molinaro and Guanyi Wang (2017); ‘‘Sparse principal component analysis and its l1-relaxation ’’ [ArXiv]

    • Rahul Mazumder, Peter Radchenko and Antoine Dedieu (2017); ‘‘Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low’’ [ArXiv]
         - Code

    • Dimitris Bertsimas, Martin Copenhaver and Rahul Mazumder (2017); ‘‘The Trimmed Lasso: Sparsity and Robustness ’’ [ArXiv]
          - Code

    • Rahul Mazumder, Diego F. Saldana and Haolei Weng (2016); ‘‘Matrix Completion with Nonconvex Regularization: Spectral Operators and Scalable Algorithms’’ [ArXiv]
          - Code

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

    Published/To Appear Methodology Papers

    • Haihao Lu and Rahul Mazumder; ‘‘Randomized Gradient Boosting Machine ’’ [ArXiv]
         SIAM Journal on Optimization (2020+)   (to appear)

    • Kinjal Basu, Amol Ghoting, Rahul Mazumder and Yao Pan; ‘‘ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications
         ICML (2020)   (to appear)

    • Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan and Rahul Mazumder; ‘‘The Tree Ensemble Layer: Differentiability meets Conditional Computation ’’ [ArXiv]
         ICML (2020)   (to appear)
         - Code

    • Hussein Hazimeh and Rahul Mazumder; ‘‘Learning Hierarchical Interactions at Scale: A Convex Optimization Approach ’’ [ArXiv]
         AISTATS (2020)   (to appear)

    • Rahul Mazumder and Haolei Weng (2020+); ‘‘Computing the degrees of freedom of rank-regularized estimators and cousins ’’ [ArXiv]
         Electronic Journal of Statistics   (to appear)

    • Yuanjun Gao, Jack Goetz, Rahul Mazumder, Matthew Connelly (2020+); ‘‘Mining Events with Declassified Diplomatic Documents ’’[ArXiv]
         Annals of Applied Statistics   (to appear)
         - Media Article

    • Hussein Hazimeh and Rahul Mazumder; ‘‘Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms ’’ [ArXiv]
         Operations Research (2020+)   (to appear)
         - Code

    • Koulik Khamaru and Rahul Mazumder; ‘‘Computation of the Maximum Likelihood estimator in low-rank Factor Analysis’’ [Journal]
         Mathematical Programming (2019)  
         - Code

    • Hari Bandi, Dimitris Bertsimas and Rahul Mazumder; ‘‘Learning a Mixture of Gaussians via Mixed Integer Optimization ’’ [Journal]
          INFORMS Journal on Optimization (2019)

    • Will Fithian and Rahul Mazumder; ‘‘Flexible Low-Rank Statistical Modeling with Missing Data and Side Information’’ [Journal]
         Statistical Science (2018)   (Special Section on Missing Data)
         Previously circulated as: ``Scalable Convex Methods for Flexible Low-Rank Matrix Modeling"

    • Rahul Mazumder, Arkopal Choudhury, Garud Iyengar and Bodhisattva Sen; ‘‘A Computational Framework for Multivariate Convex Regression and its Variants’’ [Journal]
          Journal of the American Statistical Association, Theory and Methods (2019)
          - Code

    • Dimitris Bertsimas, Martin Copenhaver and Rahul Mazumder; ‘‘Certifiably Optimal Low Rank Factor Analysis ’’ [Journal]
          Journal of Machine Learning Research (2017)
          - Code

    • Rahul Mazumder and Peter Radchenko; ‘‘The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization’’ [Journal]
          IEEE Transactions on Information Theory (2017)

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

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

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

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

    • Dimitris Bertsimas and Rahul Mazumder; ‘‘Least Quantile of Squares Regression via Modern Optimization’’ [Journal]
          Annals of Statistics (2014)

    • Rahul Mazumder and Trevor Hastie; ‘‘The Graphical Lasso: New Insights and Alternatives’’ [Journal]
         Electronic Journal of Statistics (2012)
          - Code

    • Rahul Mazumder and Trevor Hastie; ‘‘Exact covariance thresholding into connected components for large-scale Graphical Lasso’’ [Journal]
          Journal of Machine Learning Research (2012)

    • Rahul Mazumder, Trevor Hastie and Rob Tibshirani; ‘‘Spectral Regularization Algorithms for Learning Large Incomplete Matrices’’ [Journal]
          Journal of Machine Learning Research (2010)
          - Code

    • Rahul Mazumder, Jerome Friedman and Trevor Hastie; ‘‘SparseNet: Coordinate Descent with Non-Convex Penalties ’’ [Journal]
          Journal of American Statistical Association, Theory and Methods (2011)
         - Code

    • Deepak Agarwal, Liang Zhang and Rahul Mazumder; ‘‘Modeling Item-Item Similarities for Personalized Recommendations on Yahoo! Front Page’’ [Journal]
          Annals of Applied Statistics (2011)

    • Debapriya Sengupta and Rahul Mazumder; ‘‘Projected likelihood contrasts for testing homogeneity in finite mixture models with nuisance parameters’’
          Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen (2008)

    Published/To Appear Application Papers

    • Antoine Dedieu, Rahul Mazumder, Zhen Zhu and Hossein Vahabi; ‘‘Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming’’ [ArXiv]
          The 27th ACM International Conference on Information and Knowledge Management (CIKM) (2018)
          - Code

    • Julia Validomat, Rahul Mazumder, Alex McInturff, Douglas McCauley and Trevor Hastie; ‘‘Assessing the significance of global and local correlations under spatial autocorrelation: a nonparametric approach’’ [Journal]
          Biometrics (2014)

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

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

    • Rahul Mazumder; ‘‘Fluid flow pattern analysis in a trough region: a nonparametric approach’’
          Journal of Applied Statistics (2008)

    • Rahul Mazumder; ‘‘Clustering based on geometry and interactions of turbulence bursting rate processes in a trough region’’
          Environmetrics (2007)

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


  • PhD Thesis
        ‘‘Topics in Sparse Multivariate Statistics‘‘
        Stanford University (Deptartment of Statistics), 2012.
        Adviser: Trevor Hastie (Other committee members: Candes, Friedman, Tibshirani, Saunders)

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