arXiv research

New algorithm finds best subset in high-dimensional data models.

Efficient algorithm solves best subset selection problem.

A fast algorithm selects best subsets in high-dimensional models.

New suboptimal algorithm for best subset selection in high-dimensional data.

Optimizes subset selection in sparse learning problems.

abess efficiently solves various machine learning problems quickly.

Researchers expand on best subset selection theory, identifying key complexities.

EBBS integrates expert assessments into MIO best-subsets problem.

We study a seemingly unexpected and relatively less understood overfitting aspect of a fundamental tool in sparse linear modeling - best subset selection, which minimizes the residual sum of squares subject to a constraint on the number of nonzero coefficients. While the best subset selection procedure is often perceiv…

Proposes a group-splicing algorithm for efficient BSGS in high-dimensional settings.

Novel optimization method detects change points in Gaussian data.

In this paper we discuss the variable selection method from \ell0-norm constrained regression, which is equivalent to the problem of finding the best subset of a fixed size. Our study focuses on two aspects, consistency and computation. We prove that the sparse estimator from such a method can retain all of the importa…

New algorithm reduces high-dimensional data processing costs and achieves true sparsity.

A new method for sparse linear bandits reduces exploration-exploitation tradeoff.

In the last twenty-five years (1990-2014), algorithmic advances in integer optimization combined with hardware improvements have resulted in an astonishing 200 billion factor speedup in solving Mixed Integer Optimization (MIO) problems. We present a MIO approach for solving the classical best subset selection problem o…

AutoFS combines trainers to improve feature selection efficiency and effectiveness.

Interactive RL and DT feedback improve feature selection efficiency.

A DP method selects best sparse models in high dimensions efficiently.

In life sciences, the experts generally use empirical knowledge to recode variables, choose interactions and perform selection by classical approach. The aim of this work is to perform automatic learning algorithm for variables selection which can lead to know if experts can be help in they decision or simply replaced …

Improves convergence speed in compressive sensing with a new probabilistic approach.

New method solves sparse approximation problem using trimmed lasso and generalized soft-min penalties.

This paper uses Reinforcement Learning to select features from a large dataset.

In this article, we propose a new algorithm for supervised learning methods, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, an ideal selection method should have the potential of adding a supplementary level of regres…

Algorithm selects variables and bandwidths for geographically weighted regression.

The $L_0$-regularized least squares problem (a.k.a. best subsets) is central to sparse statistical learning and has attracted significant attention across the wider statistics, machine learning, and optimization communities. Recent work has shown that modern mixed integer optimization (MIO) solvers can be used to addre…

Paper introduces DP methods for high-dimensional variable selection.

Stagewise boosting improves gradient boosting for distributional regression.

We consider the problem of identifying any $k$ out of the best $m$ arms in an $n$-armed stochastic multi-armed bandit. Framed in the PAC setting, this particular problem generalises both the problem of `best subset selection' and that of selecting `one out of the best m' arms [arcsk 2017]. In applications such as crowd…

Bayesian method selects subsets for LMMs with structured dependence.

This paper uses MIO to select features for kernel SVM classification.

Holistic GLMs add constraints for better model quality.

New methods for selecting variables in complex biomedical data.

Method selects significant spatial covariates in noisy data.

New method selects features via tensor decomposition and submodular optimization.

Bayesian approach selects subsets of variables for interpretable prediction and identifies key factors in educational outcomes.

Dimensionality reduction is a first step of many machine learning pipelines. Two popular approaches are principal component analysis, which projects onto a small number of well chosen but non-interpretable directions, and feature selection, which selects a small number of the original features. Feature selection can be…

In this paper, we investigate the possibility of improving the performance of multi-objective optimization solution approaches using machine learning techniques. Specifically, we focus on multi-objective binary linear programs and employ one of the most effective and recently developed criterion space search algorithms…

Approximate Bayesian Computation is widely used in systems biology for inferring parameters in stochastic gene regulatory network models. Its performance hinges critically on the ability to summarize high-dimensional system responses such as time series into a few informative, low-dimensional summary statistics. The qu…

In a regression setting we propose algorithms that reduce the dimensionality of the features while simultaneously maximizing a statistical measure of dependence known as distance correlation between the low-dimensional features and a response variable. This helps in solving the prediction problem with a low-dimensional…

Transportation modes prediction is a fundamental task for decision making in smart cities and traffic management systems. Traffic policies designed based on trajectory mining can save money and time for authorities and the public. It may reduce the fuel consumption and commute time and moreover, may provide more pleasa…

To find efficient screening methods for high dimensional linear regression models, this paper studies the relationship between model fitting and screening performance. Under a sparsity assumption, we show that a subset that includes the true submodel always yields smaller residual sum of squares (i.e., has better model…

A novel method for efficient CDRL over wireless networks.

We consider the problem of finding critical points of functions that are non-convex and non-smooth. Studying a fairly broad class of such problems, we analyze the behavior of three gradient-based methods (gradient descent, proximal update, and Frank-Wolfe update). For each of these methods, we establish rates of conver…

This paper uses quantum computing to solve sparse linear regression problems efficiently.

Given a linear regression setting, Iterative Least Trimmed Squares (ILTS) involves alternating between (a) selecting the subset of samples with lowest current loss, and (b) re-fitting the linear model only on that subset. Both steps are very fast and simple. In this paper we analyze ILTS in the setting of mixed linear …

Graph Canonical Correlation Analysis improves CCA for multiomics datasets.

A new gradient estimator reduces variance near boundaries for binary latent variables.

Develops an algorithm to find the best subset of points for maximizing the coefficient of determination.