A fast method for Lasso and Logistic Lasso problems.
problem Solving Lasso and Logistic Lasso regression problems efficiently.
method Iterative active set approach using solver updates.
result 31.41 times faster on average for compressed sensing.
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48 results for “logistic LASSO regression”
New weighted Lasso estimates improve logistic regression performance with measurement error.
problem Improper Lasso estimates in sparse logistic regression with equal penalties.
method Proposed weighted Lasso estimates using McDiarmid inequality for non-asymptotic oracle inequalities.
result Finite sample behavior illustrated by non-asymptotic oracle inequalities for estimation and prediction errors.
Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selectionstat.CO
High-dimensional feature selection arises in many areas of modern science. For example, in genomic research we want to find the genes that can be used to separate tissues of different classes (e.g. cancer and normal) from tens of thousands of genes that are active (expressed) in certain tissue cells. To this end, we wi…
We apply the network Lasso to solve binary classification and clustering problems for network-structured data. To this end, we generalize ordinary logistic regression to non-Euclidean data with an intrinsic network structure. The resulting "logistic network Lasso" amounts to solving a non-smooth convex regularized empi…
Sparse multinomial logistic regression for multiclass classification with feature selection.
problem High-dimensional multiclass classification with a focus on sparse models.
method Penalized maximum likelihood with complexity penalty, feature selection using group Lasso and Slope classifiers.
result Achievement of minimax order in both small and large number of classes regimes.
Network Lasso classifies partially labeled data with high-dimensional features.
problem Classifying data points with limited labeled data and high-dimensional features.
method Logistic Network Lasso using total variation regularization and primal-dual splitting.
result Accurate classification achieved from limited labeled data via network structure.
Improved Frank-Wolfe algorithm speeds up training of differentially private LASSO models.
problem Training differentially private LASSO models on sparse data.
method Adapted Frank-Wolfe algorithm for sparse inputs, reducing runtime.
result Training time reduced from to .
The Hessian Screening Rulestat.ML
A new screening rule improves lasso solving speed.
problem Efficiently solving lasso problems with high correlation.
method Uses second-order information from the Hessian to screen predictors.
result Outperforms alternatives on simulated and real data.
We develop a first order expansion for convex penalized estimators in high-dimensional regression.
problem High-dimensional regression problems with random designs.
method Construct a first order expansion of the penalized estimator .
result The risk of is asymptotically the same as the risk of .
SAGA is a fast incremental gradient method on the finite sum problem and its effectiveness has been tested on a vast of applications. In this paper, we analyze SAGA on a class of non-strongly convex and non-convex statistical problem such as Lasso, group Lasso, Logistic regression with regularization, linear r…
In this paper, we consider stochastic dual coordinate (SDCA) {\em without} strongly convex assumption or convex assumption. We show that SDCA converges linearly under mild conditions termed restricted strong convexity. This covers a wide array of popular statistical models including Lasso, group Lasso, and logistic reg…
In this work, we consider a manufactory process which can be described by a multiple-instance logistic regression model. In order to compute the maximum likelihood estimation of the unknown coefficient, an expectation-maximization algorithm is proposed, and the proposed modeling approach can be extended to identify the…
Method extracts features from signals for classification with explainability.
problem Lack of interpretability in signal classification models.
method Combining scattering transform and multiclass logistic regression with zeroth-order optimization.
result Uncovered the meaning of scattering transform coefficients.
Deep learning predicts asthma ED visits better than traditional methods.
problem Predicting asthma-related ED visits to improve patient management.
method Deep learning (Artificial Neural Networks) compared to Lasso logistic regression.
result Deep learning model (ANN) outperforms traditional Lasso logistic regression (AUC = 0.845 vs. AUC = 0.842).
Neural networks outperform logistic regression for predicting HF readmission.
problem Predicting 30-day all-cause readmission in heart failure patients.
method Used a large administrative claims dataset to compare neural network models (RNNCRF) with logistic regression models (LASSO) for predicting readmission.
result RNNCRF model achieved best performance with 0.642 AUC, while logistic regression with LASSO had equal performance.
The -penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weigh…
Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has been proposed as a way of extending the ideas of the lasso to the problem of group…
Enhances tensor regression for interpretability and performance.
problem Interpreting and modeling multidimensional tensor data with structural heterogeneity.
method Generalized Nonnegative Structured Kruskal Tensor Regression (NS-KTR) with hybrid regularization and nonnegativity constraints.
result NS-KTR outperforms conventional methods in synthetic and real hyperspectral datasets.
FILTER model uses fusion penalized logistic threshold regression for high-dimensional data with unknown cut points.
problem Modeling high-dimensional data with unknown cut points and binary responses.
method Fusion penalized logistic threshold regression (FILTER) model with fused lasso penalty for variable selection.
result Established non-asymptotic error bounds for coefficient estimation and model selection consistency.
We introduce a proximal version of the stochastic dual coordinate ascent method and show how to accelerate the method using an inner-outer iteration procedure. We analyze the runtime of the framework and obtain rates that improve state-of-the-art results for various key machine learning optimization problems including …
Optimal sketching bounds for sparse linear regression under various loss functions are established.
problem Sparse linear regression under different loss functions.
method Distribution over oblivious sketches for sparse norm regression and hinge-like loss functions.
result Optimal sketching bounds with rows for sparse norm regression and rows for hinge-like loss functions.
Develops a distributed least squares approximation method for regression problems.
problem Solving large-scale regression problems on distributed systems.
method Approximates local objective functions using a local quadratic form and combines estimators by weighted average.
result Statistically efficient combined estimator with one round of communication.
We investigate a robust penalized logistic regression algorithm based on a minimum distance criterion. Influential outliers are often associated with the explosion of parameter vector estimates, but in the context of standard logistic regression, the bias due to outliers always causes the parameter vector to implode, t…
Model predicts higher education dropout risk with interpretable parameters.
problem Predicting and understanding student dropout risk in higher education.
method Sparse interpretable post-clustering logistic regression.
result Model identifies distinct dropout risk subgroups within the student population.
Hybrid machine learning improves gallstone risk prediction.
problem Complex gallstone disease risk factors and interactions.
method Adaptive LASSO for variable selection, BART for interactions, differential equations for interpretation.
result Enhanced prediction accuracy and actionable insights.
In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term. A recently introduced technique called screening rules propose to ignore some variables in the optimization leveraging the expected sparsity of the solutions and consequently leading to faster s…
SVRG and its variants are among the state of art optimization algorithms for large scale machine learning problems. It is well known that SVRG converges linearly when the objective function is strongly convex. However this setup can be restrictive, and does not include several important formulations such as Lasso, grou…
Proposes MM-DUST for efficient generalized lasso solution paths.
problem Efficiently solve generalized lasso problems in large-scale and non-linear models.
method Majorization-minimization dual stagewise algorithm incorporating quadratic majorizers and stagewise learning.
result Established the uniform convergence of approximated solution paths.
Method meta-classifies semantic segmentation predictions using dispersion measures.
problem Assessing the quality of semantic segmentation predictions.
method Aggregates dispersion measures (entropy) of predicted probabilities to derive metrics correlated with IoU.
result Metrics correlate well with IoU, providing reliable prediction quality ratings.
New algorithm recovers sparse binary vectors from generalized linear measurements efficiently.
problem Recovering sparse binary vectors from generalized linear measurements.
method Linear estimation algorithm and information theoretic lower bounds.
result Optimal sample complexity of for noisy one bit quantized linear measurements.
PLS-Lasso integrates dimension reduction into regression for financial index tracking.
problem Dimension reduction and regression are traditionally treated separately in multivariate data analysis.
method PLS-Lasso integrates dimension reduction directly into the regression process, presenting two formulations: PLS-Lasso-v1 and PLS-Lasso-v2.
result PLS-Lasso-v1 and PLS-Lasso-v2 outperform Lasso in financial index tracking.
Lasso proves consistent model selection for high-dimensional Ising models.
problem Model selection consistency of Lasso for high-dimensional Ising models.
method Theoretical analysis of Lasso with and without post-thresholding for Ising models.
result Lasso without post-thresholding is model selection consistent in the whole paramagnetic phase with .
Solving l1 regularized optimization problems is common in the fields of computational biology, signal processing and machine learning. Such l1 regularization is utilized to find sparse minimizers of convex functions. A well-known example is the LASSO problem, where the l1 norm regularizes a quadratic function. A multil…
Study evaluates different meta-learners for multi-view stacking.
problem Choosing the best meta-learner for multi-view stacking.
method Seven different meta-learners were evaluated in simulations and real data.
result Nonnegative lasso, nonnegative adaptive lasso, and nonnegative elastic net are suitable meta-learners.
New lower bounds improve logistic log-likelihood optimization and inference.
problem Designing computationally tractable lower bounds for logistic log-likelihoods.
method Developed a piece-wise quadratic lower bound that uniformly improves tangent quadratic minorizers.
result Improves the speed of convergence and accuracy of variational Bayes approximations.
Paper finds a lower bound for estimating low-rank matrices in logistic regression.
problem Estimating low-rank coefficient matrices in logistic regression.
method Derives a minimax lower bound on the risk.
result The bound depends on matrix dimensions, rank, and sample size.
We consider a wide range of regularized stochastic minimization problems with two regularization terms, one of which is composed with a linear function. This optimization model abstracts a number of important applications in artificial intelligence and machine learning, such as fused Lasso, fused logistic regression, a…
Coordinate descent (CD) algorithms have become the method of choice for solving a number of optimization problems in machine learning. They are particularly popular for training linear models, including linear support vector machine classification, LASSO regression, and logistic regression. We consider general CD with …
Develops a tool to identify abnormal blood smear results based on CBC tests.
problem Manual review of blood smears by technologists is time-consuming and inconsistent.
method Cost-sensitive Lasso-penalized additive logistic regression combined with stability selection.
result The tool correctly identifies true cutoff values for abnormal smear results.
Novel bounds for logistic regression coreset construction and feature selection.
problem Efficiently summarize and reduce logistic regression inputs.
method Feature space sketching for logistic regression.
result Tight bounds for coreset construction and feature selection.
Unified framework for sparse logistic regression with nonconvex regularization.
problem Sparse logistic regression with nonconvex regularization.
method Unified framework, line search criteria for nonconvex terms.
result Effective classification and feature selection at lower computational cost.
Bayesian Lasso Sparse model provides sparse estimates in linear and nonlinear regression.
problem Sparse learning in regression models.
method Develops a new sparse learning model using type-II maximum likelihood procedure.
result The BLS model provides sparse estimates and is more precise, especially with noisy data.
Paper examines LASSO for high-dimensional predictive regression, improving its performance in forecasting unemployment.
problem High-dimensional predictive regression with many predictors and unit roots.
method LASSO with new probabilistic bounds for consistency.
result LASSO maintains its asymptotic guarantee with standardized predictors and improves forecasting of unemployment.
Improves logistic regression performance on imbalanced data.
problem Imbalanced data leads to all labels being estimated as majority class.
method Uses F-measure optimization to estimate relative density ratio and approximate relative F-measure.
result Proposed method improves logistic regression performance on imbalanced data.
Recently, (Blanchet, Kang, and Murhy 2016, and Blanchet, and Kang 2017) showed that several machine learning algorithms, such as square-root Lasso, Support Vector Machines, and regularized logistic regression, among many others, can be represented exactly as distributionally robust optimization (DRO) problems. The dist…
Prevalidated ridge regression simplifies logistic regression for high-dimensional data.
problem Efficient probabilistic classification in high-dimensional data with logistic regression.
method Developed a prevalidated ridge regression model that matches logistic regression's performance but is more computationally efficient.
result Prevalidated ridge regression achieves similar classification error and log-loss to logistic regression for high-dimensional data.
Improved sketching for logistic and regression with near-linear dimensions.
problem Efficiently approximate and logistic regression problems.
method New sketching techniques achieving near-linear dimensions for both problems.
result Achieved near-linear sketching dimensions for and logistic regression.
We analyze coresets for regularized regression problems and propose a modified lasso that yields smaller coresets.
problem Analyzing coresets for regularized regression problems.
method Examined coresets for ridge regression and proposed a modified lasso problem.
result No coreset for regularized regression can be smaller than the unregularized version when .