
Integration of Survival Data from Multiple Studies
We introduce a statistical procedure that integrates survival data from ...
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Multivariate Convex Regression at Scale
We present new largescale algorithms for fitting a multivariate convex ...
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The Tree Ensemble Layer: Differentiability meets Conditional Computation
Neural networks and tree ensembles are stateoftheart learners, each w...
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Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
We consider a discrete optimization based approach for learning sparse c...
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Computing the degrees of freedom of rankregularized estimators and cousins
Estimating a low rank matrix from its linear measurements is a problem o...
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Computing Estimators of Dantzig Selector type via Column and Constraint Generation
We consider a class of linearprogramming based estimators in reconstruc...
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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...
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Solving largescale L1regularized SVMs and cousins: the surprising effectiveness of column and constraint generation
The linear Support Vector Machine (SVM) is one of the most popular binar...
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Randomized Gradient Boosting Machine
Gradient Boosting Machine (GBM) introduced by Friedman is an extremely p...
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Condition Number Analysis of Logistic Regression, and its Implications for Standard FirstOrder Solution Methods
Logistic regression is one of the most popular methods in binary classif...
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Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
We consider the canonical L_0regularized least squares problem (aka bes...
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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 online...
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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, ...
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Computation of the Maximum Likelihood estimator in lowrank Factor Analysis
Factor analysis, a classical multivariate statistical technique is popul...
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Mining Events with Declassified Diplomatic Documents
Since 1973 the State Department has been using electronic records system...
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The Trimmed Lasso: Sparsity and Robustness
Nonconvex penalty methods for sparse modeling in linear regression have ...
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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...
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An Extended FrankWolfe Method with "InFace" Directions, and its Application to LowRank Matrix Completion
Motivated principally by the lowrank matrix completion problem, we pres...
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The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization
We propose a novel highdimensional linear regression estimator: the Dis...
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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...
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Matrix Completion and LowRank SVD via Fast Alternating Least Squares
The matrixcompletion problem has attracted a lot of attention, largely ...
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Flexible LowRank Statistical Modeling with Side Information
We propose a general framework for reducedrank modeling of matrixvalue...
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AdaBoost and Forward Stagewise Regression are FirstOrder Convex Optimization Methods
Boosting methods are highly popular and effective supervised learning me...
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The Graphical Lasso: New Insights and Alternatives
The graphical lasso FHT2007a is an algorithm for learning the structure ...
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A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso
We propose a scalable, efficient and statistically motivated computation...
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Exact covariance thresholding into connected components for largescale Graphical Lasso
We consider the sparse inverse covariance regularization problem or grap...
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Rahul Mazumder
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Assistant Professor of Operations Research and Statistics at Massachusetts Institute of Technology