Rahul Mazumder

research

∙ 05/19/2022

Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles

Decision tree ensembles are widely used and competitive learning models....

Shibal Ibrahim, et al. ∙

research

∙ 02/10/2022

L0Learn: A Scalable Package for Sparse Learning using L0 Regularization

We introduce L0Learn: an open-source package for sparse regression and c...

Hussein Hazimeh, et al. ∙

research

∙ 10/13/2021

Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance

Fine-tuning of large pre-trained image and language models on small cust...

Shibal Ibrahim, et al. ∙

research

∙ 09/23/2021

Sparse PCA: A New Scalable Estimator Based On Integer Programming

We consider the Sparse Principal Component Analysis (SPCA) problem under...

Kayhan Behdin, et al. ∙

research

∙ 09/19/2021

Optimal Ensemble Construction for Multi-Study Prediction with Applications to COVID-19 Excess Mortality Estimation

It is increasingly common to encounter prediction tasks in the biomedica...

Gabriel Loewinger, et al. ∙

research

∙ 08/24/2021

Predicting Census Survey Response Rates via Interpretable Nonparametric Additive Models with Structured Interactions

Accurate and interpretable prediction of survey response rates is import...

Shibal Ibrahim, et al. ∙

research

∙ 07/18/2021

Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach

We explore computational aspects of maximum likelihood estimation of the...

Haoyue Wang, et al. ∙

research

∙ 06/03/2021

Linear regression with partially mismatched data: local search with theoretical guarantees

Linear regression is a fundamental modeling tool in statistics and relat...

Rahul Mazumder, et al. ∙

research

∙ 05/24/2021

A new computational framework for log-concave density estimation

In Statistics, log-concave density estimation is a central problem withi...

Wenyu Chen, et al. ∙

research

∙ 04/14/2021

Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives

We present a new algorithmic framework for grouped variable selection th...

Hussein Hazimeh, et al. ∙

research

∙ 04/08/2021

Archetypal Analysis for Sparse Nonnegative Matrix Factorization: Robustness Under Misspecification

We consider the problem of sparse nonnegative matrix factorization (NMF)...

Kayhan Behdin, et al. ∙

research

∙ 07/16/2020

Integration of Survival Data from Multiple Studies

We introduce a statistical procedure that integrates survival data from ...

Steffen Ventz, et al. ∙

research

∙ 05/23/2020

Multivariate Convex Regression at Scale

We present new large-scale algorithms for fitting a multivariate convex ...

Wenyu Chen, et al. ∙

research

∙ 02/18/2020

The Tree Ensemble Layer: Differentiability meets Conditional Computation

Neural networks and tree ensembles are state-of-the-art learners, each w...

Hussein Hazimeh, et al. ∙

research

∙ 01/17/2020

Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives

We consider a discrete optimization based approach for learning sparse c...

Antoine Dedieu, et al. ∙

research

∙ 09/23/2019

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

Estimating a low rank matrix from its linear measurements is a problem o...

Rahul Mazumder, et al. ∙

research

∙ 08/18/2019

Computing Estimators of Dantzig Selector type via Column and Constraint Generation

We consider a class of linear-programming based estimators in reconstruc...

Rahul Mazumder, et al. ∙

research

∙ 02/05/2019

Learning Hierarchical Interactions at Scale: A Convex Optimization Approach

In many learning settings, it is beneficial to augment the main features...

Hussein Hazimeh, et al. ∙

research

∙ 01/06/2019

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...

Antoine Dedieu, et al. ∙

research

∙ 10/24/2018

Randomized Gradient Boosting Machine

Gradient Boosting Machine (GBM) introduced by Friedman is an extremely p...

Haihao Lu, et al. ∙

research

∙ 10/20/2018

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...

Robert M. Freund, et al. ∙

research

∙ 03/05/2018

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

We consider the canonical L_0-regularized least squares problem (aka bes...

Hussein Hazimeh, et al. ∙

research

∙ 03/04/2018

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

An important metric of users' satisfaction and engagement within on-line...

Antoine Dedieu, et al. ∙

research

∙ 01/24/2018

Matrix Completion with Nonconvex Regularization: Spectral Operators and Scalable Algorithms

In this paper, we study the popularly dubbed matrix completion problem, ...

Rahul Mazumder, et al. ∙

research

∙ 01/18/2018

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

Factor analysis, a classical multivariate statistical technique is popul...

Koulik Khamaru, et al. ∙

research

∙ 12/20/2017

Mining Events with Declassified Diplomatic Documents

Since 1973 the State Department has been using electronic records system...

Yuanjun Gao, et al. ∙

research

∙ 08/15/2017

The Trimmed Lasso: Sparsity and Robustness

Nonconvex penalty methods for sparse modeling in linear regression have ...

Dimitris Bertsimas, et al. ∙

research

∙ 08/10/2017

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

We study the behavior of a fundamental tool in sparse statistical modeli...

Rahul Mazumder, et al. ∙

research

∙ 11/06/2015

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...

Robert M. Freund, et al. ∙

research

∙ 08/08/2015

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

We propose a novel high-dimensional linear regression estimator: the Dis...

Rahul Mazumder, et al. ∙

research

∙ 05/16/2015

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...

Robert M. Freund, et al. ∙

research

∙ 10/09/2014

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

The matrix-completion problem has attracted a lot of attention, largely ...

Trevor Hastie, et al. ∙

research

∙ 08/20/2013

Flexible Low-Rank Statistical Modeling with Side Information

We propose a general framework for reduced-rank modeling of matrix-value...

William Fithian, et al. ∙

research

∙ 07/04/2013

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

Boosting methods are highly popular and effective supervised learning me...

Robert M. Freund, et al. ∙

research

∙ 11/23/2011

The Graphical Lasso: New Insights and Alternatives

The graphical lasso FHT2007a is an algorithm for learning the structure ...

Rahul Mazumder, et al. ∙

research

∙ 10/25/2011

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

We propose a scalable, efficient and statistically motivated computation...

Rahul Mazumder, et al. ∙

research

∙ 08/18/2011

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

We consider the sparse inverse covariance regularization problem or grap...

Rahul Mazumder, et al. ∙

Rahul Mazumder

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