Peer Reviewed Publications

Published Methodology Papers

• ‘‘ Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares’’, Trevor Hastie, Rahul Mazumder, Jason Lee, Reza Zadeh; (2015) to appear, 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).

Preprints / Submitted

• ‘‘ The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization ’’, Rahul Mazumder, Peter Radchenko; 2015. ArXiv

• ‘‘A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives ’’, Robert M. Freund, Paul Grigas, Rahul Mazumder; 2015. ArXiv

• ‘‘ Subset Selection in Regression via Modern Optimization ’’, Dimitris Bertsimas, Angela King and Rahul Mazumder; 2014. ArXiv

• ‘‘Factor Analysis via a Modern Optimization Lens’’, Dimitris Bertsimas and Rahul Mazumder; 2014.

• ‘‘Scalable Convex Methods for Flexible Low-Rank Matrix Modeling’’, Will Fithian and Rahul Mazumder; 2013, 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

Theses

• 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.