research
∙
07/18/2023
Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives
We consider the problem of learning a sparse graph underlying an undirec...
share
research
∙
06/12/2023
FIRE: An Optimization Approach for Fast Interpretable Rule Extraction
We present FIRE, Fast Interpretable Rule Extraction, an optimization-bas...
research
∙
06/05/2023
COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search
The sparse Mixture-of-Experts (Sparse-MoE) framework efficiently scales ...
research
∙
06/04/2023
Matrix Completion from General Deterministic Sampling Patterns
Most of the existing works on provable guarantees for low-rank matrix co...
research
∙
02/28/2023
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
The sheer size of modern neural networks makes model serving a serious c...
research
∙
02/23/2023
Sharpness-Aware Minimization: An Implicit Regularization Perspective
Sharpness-Aware Minimization (SAM) is a recent optimization framework ai...
research
∙
12/16/2022
Multi-Task Learning for Sparsity Pattern Heterogeneity: A Discrete Optimization Approach
We extend best-subset selection to linear Multi-Task Learning (MTL), whe...
research
∙
08/24/2022
Pushing the limits of fairness impossibility: Who's the fairest of them all?
The impossibility theorem of fairness is a foundational result in the al...
research
∙
06/23/2022
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features
Decision trees are one of the most useful and popular methods in the mac...
research
∙
05/31/2022
ForestPrune: Compact Depth-Controlled Tree Ensembles
Tree ensembles are versatile supervised learning algorithms that achieve...
research
∙
05/19/2022
Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles
Decision tree ensembles are widely used and competitive learning models....
research
∙
02/10/2022
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization
We present L0Learn: an open-source package for sparse linear regression ...
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...
research
∙
09/23/2021
Sparse PCA: A New Scalable Estimator Based On Integer Programming
We consider the Sparse Principal Component Analysis (SPCA) problem under...
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...
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...
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...
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...
research
∙
05/24/2021
A new computational framework for log-concave density estimation
In Statistics, log-concave density estimation is a central problem withi...
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...
research
∙
04/08/2021
Archetypal Analysis for Sparse Nonnegative Matrix Factorization: Robustness Under Misspecification
We consider the problem of sparse nonnegative matrix factorization (NMF)...
research
∙
07/16/2020
Integration of Survival Data from Multiple Studies
We introduce a statistical procedure that integrates survival data from ...
research
∙
05/23/2020
Multivariate Convex Regression at Scale
We present new large-scale algorithms for fitting a multivariate convex ...
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...
research
∙
01/17/2020
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
We consider a discrete optimization based approach for learning sparse c...
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...
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...
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...
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...
research
∙
10/24/2018
Randomized Gradient Boosting Machine
Gradient Boosting Machine (GBM) introduced by Friedman is an extremely p...
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...
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...
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...
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, ...
research
∙
01/18/2018
Computation of the Maximum Likelihood estimator in low-rank Factor Analysis
Factor analysis, a classical multivariate statistical technique is popul...
research
∙
12/20/2017
Mining Events with Declassified Diplomatic Documents
Since 1973 the State Department has been using electronic records system...
research
∙
08/15/2017
The Trimmed Lasso: Sparsity and Robustness
Nonconvex penalty methods for sparse modeling in linear regression have ...
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...
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...
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...
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...
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 ...
research
∙
08/20/2013
Flexible Low-Rank Statistical Modeling with Side Information
We propose a general framework for reduced-rank modeling of matrix-value...
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...
research
∙
11/23/2011
The Graphical Lasso: New Insights and Alternatives
The graphical lasso FHT2007a is an algorithm for learning the structure ...
research
∙
10/25/2011
A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso
We propose a scalable, efficient and statistically motivated computation...
research
∙
08/18/2011
