Rahul Mazumder

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MIT ∙ Stanford University

Assistant Professor of Operations Research and Statistics at Massachusetts Institute of Technology

Integration of Survival Data from Multiple Studies

We introduce a statistical procedure that integrates survival data from ...
07/16/2020 ∙ by Steffen Ventz, et al. ∙ 0 ∙ share

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Multivariate Convex Regression at Scale

We present new large-scale algorithms for fitting a multivariate convex ...
05/23/2020 ∙ by Wenyu Chen, et al. ∙ 0 ∙ share

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The Tree Ensemble Layer: Differentiability meets Conditional Computation

Neural networks and tree ensembles are state-of-the-art learners, each w...
02/18/2020 ∙ by Hussein Hazimeh, et al. ∙ 1 ∙ share

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Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives

We consider a discrete optimization based approach for learning sparse c...
01/17/2020 ∙ by Antoine Dedieu, et al. ∙ 8 ∙ share

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Computing the degrees of freedom of rank-regularized estimators and cousins

Estimating a low rank matrix from its linear measurements is a problem o...
09/23/2019 ∙ by Rahul Mazumder, et al. ∙ 0 ∙ share

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Computing Estimators of Dantzig Selector type via Column and Constraint Generation

We consider a class of linear-programming based estimators in reconstruc...
08/18/2019 ∙ by Rahul Mazumder, et al. ∙ 0 ∙ share

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Learning Hierarchical Interactions at Scale: A Convex Optimization Approach

In many learning settings, it is beneficial to augment the main features...
02/05/2019 ∙ by Hussein Hazimeh, et al. ∙ 1 ∙ share

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Solving large-scale L1-regularized SVMs and cousins: the surprising effectiveness of column and constraint generation

The linear Support Vector Machine (SVM) is one of the most popular binar...
01/06/2019 ∙ by Antoine Dedieu, et al. ∙ 0 ∙ share

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Randomized Gradient Boosting Machine

Gradient Boosting Machine (GBM) introduced by Friedman is an extremely p...
10/24/2018 ∙ by Haihao Lu, et al. ∙ 0 ∙ share

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Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods

Logistic regression is one of the most popular methods in binary classif...
10/20/2018 ∙ by Robert M. Freund, et al. ∙ 0 ∙ share

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Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms

We consider the canonical L_0-regularized least squares problem (aka bes...
03/05/2018 ∙ by Hussein Hazimeh, et al. ∙ 1 ∙ share

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Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming

An important metric of users' satisfaction and engagement within on-line...
03/04/2018 ∙ by Antoine Dedieu, et al. ∙ 0 ∙ share

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Matrix Completion with Nonconvex Regularization: Spectral Operators and Scalable Algorithms

In this paper, we study the popularly dubbed matrix completion problem, ...
01/24/2018 ∙ by Rahul Mazumder, et al. ∙ 0 ∙ share

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Computation of the Maximum Likelihood estimator in low-rank Factor Analysis

Factor analysis, a classical multivariate statistical technique is popul...
01/18/2018 ∙ by Koulik Khamaru, et al. ∙ 0 ∙ share

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Mining Events with Declassified Diplomatic Documents

Since 1973 the State Department has been using electronic records system...
12/20/2017 ∙ by Yuanjun Gao, et al. ∙ 0 ∙ share

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The Trimmed Lasso: Sparsity and Robustness

Nonconvex penalty methods for sparse modeling in linear regression have ...
08/15/2017 ∙ by Dimitris Bertsimas, et al. ∙ 0 ∙ share

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Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low

We study the behavior of a fundamental tool in sparse statistical modeli...
08/10/2017 ∙ by Rahul Mazumder, et al. ∙ 0 ∙ share

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An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion

Motivated principally by the low-rank matrix completion problem, we pres...
11/06/2015 ∙ by Robert M. Freund, et al. ∙ 0 ∙ share

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The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization

We propose a novel high-dimensional linear regression estimator: the Dis...
08/08/2015 ∙ by Rahul Mazumder, et al. ∙ 0 ∙ share

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A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives

In this paper we analyze boosting algorithms in linear regression from a...
05/16/2015 ∙ by Robert M. Freund, et al. ∙ 0 ∙ share

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Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares

The matrix-completion problem has attracted a lot of attention, largely ...
10/09/2014 ∙ by Trevor Hastie, et al. ∙ 0 ∙ share

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Flexible Low-Rank Statistical Modeling with Side Information

We propose a general framework for reduced-rank modeling of matrix-value...
08/20/2013 ∙ by William Fithian, et al. ∙ 0 ∙ share

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AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods

Boosting methods are highly popular and effective supervised learning me...
07/04/2013 ∙ by Robert M. Freund, et al. ∙ 0 ∙ share

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The Graphical Lasso: New Insights and Alternatives

The graphical lasso FHT2007a is an algorithm for learning the structure ...
11/23/2011 ∙ by Rahul Mazumder, et al. ∙ 0 ∙ share

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A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso

We propose a scalable, efficient and statistically motivated computation...
10/25/2011 ∙ by Rahul Mazumder, et al. ∙ 0 ∙ share

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Exact covariance thresholding into connected components for large-scale Graphical Lasso

We consider the sparse inverse covariance regularization problem or grap...
08/18/2011 ∙ by Rahul Mazumder, et al. ∙ 0 ∙ share

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Rahul Mazumder

Featured Co-authors

Dimitris Bertsimas

Trevor Hastie

Jason Lee

Haihao Lu

Zhen Zhu

Stephen Wright

Antoine Dedieu

William Fithian

Lorenzo Trippa

Paul Grigas

Koulik Khamaru

Integration of Survival Data from Multiple Studies

Multivariate Convex Regression at Scale

The Tree Ensemble Layer: Differentiability meets Conditional Computation

Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives

Computing the degrees of freedom of rank-regularized estimators and cousins

Computing Estimators of Dantzig Selector type via Column and Constraint Generation

Learning Hierarchical Interactions at Scale: A Convex Optimization Approach

Solving large-scale L1-regularized SVMs and cousins: the surprising effectiveness of column and constraint generation

Randomized Gradient Boosting Machine

Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods

Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms

Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming

Matrix Completion with Nonconvex Regularization: Spectral Operators and Scalable Algorithms

Computation of the Maximum Likelihood estimator in low-rank Factor Analysis

Mining Events with Declassified Diplomatic Documents

The Trimmed Lasso: Sparsity and Robustness

Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low

An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion

The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization

A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives

Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares

Flexible Low-Rank Statistical Modeling with Side Information

AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods

The Graphical Lasso: New Insights and Alternatives

A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso

Exact covariance thresholding into connected components for large-scale Graphical Lasso