Rejoinder to "Least angle regression" by Efron et al

Abstract

Rejoinder to ``Least angle regression'' by Efron et al. [math.ST/0406456]

References in corpus (1)

• Piecewise linear regularized solution paths

Cited by in corpus (1065)

• Scikit-learn: Machine Learning in Python
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• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• High-dimensional graphs and variable selection with the Lasso
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• Message Passing Algorithms for Compressed Sensing
• Pathwise coordinate optimization
• BART: Bayesian additive regression trees
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• Compressed Sensing of Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
• A survey of sparse representation: algorithms and applications
• Spike and slab variable selection: Frequentist and Bayesian strategies
• A Survey on the Explainability of Supervised Machine Learning
• On the "degrees of freedom" of the lasso
• A high-bias, low-variance introduction to Machine Learning for physicists
• On the adaptive elastic-net with a diverging number of parameters
• Task-Driven Dictionary Learning
• Boosting Algorithms: Regularization, Prediction and Model Fitting
• Supervised Dictionary Learning
• Consistency of the group Lasso and multiple kernel learning
• One-step sparse estimates in nonconcave penalized likelihood models
• Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
• The composite absolute penalties family for grouped and hierarchical variable selection
• Templates for Convex Cone Problems with Applications to Sparse Signal Recovery
• Controlling the false discovery rate via knockoffs
• Component selection and smoothing in multivariate nonparametric regression
• A significance test for the lasso
• The solution path of the generalized lasso
• Wild binary segmentation for multiple change-point detection
• Variable selection in nonparametric additive models
• A Survey of Multi-View Representation Learning
• Exact post-selection inference, with application to the lasso
• The sparsity and bias of the Lasso selection in high-dimensional linear regression
• Estimating treatment effect heterogeneity in randomized program evaluation
• Sparse and stable Markowitz portfolios
• Piecewise linear regularized solution paths
• Lasso-type recovery of sparse representations for high-dimensional data
• A non-adapted sparse approximation of PDEs with stochastic inputs
• Structured Variable Selection with Sparsity-Inducing Norms
• Extremal Quantile Regression: An Overview
• Model Selection Techniques -- An Overview
• The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling
• Optimization Problems for Machine Learning: A Survey
• High-dimensional variable selection
• Support union recovery in high-dimensional multivariate regression
• A lasso for hierarchical interactions
• "Why Should I Trust You?": Explaining the Predictions of Any Classifier
• Model Selection in Linear Mixed Models
• Bayesian variable selection regression for genome-wide association studies and other large-scale problems
• Supersparse Linear Integer Models for Optimized Medical Scoring Systems
• Strong oracle optimality of folded concave penalized estimation
• Approximation and learning by greedy algorithms
• Boosting for high-dimensional linear models
• Robust rank correlation based screening
• Network exploration via the adaptive LASSO and SCAD penalties
• Optimally Tuned Iterative Reconstruction Algorithms for Compressed Sensing
• A Selective Review of Group Selection in High-Dimensional Models
• An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis
• Learning with Structured Sparsity
• A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
• Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality
• Proximal Methods for Hierarchical Sparse Coding
• Active learning for structural reliability: survey, general framework and benchmark
• Compressive estimation of doubly selective channels in multicarrier systems: Leakage effects and sparsity-enhancing processing
• Robust Wasserstein Profile Inference and Applications to Machine Learning
• Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset
• Compressed Sensing in Astronomy
• Multi-Label Prediction via Compressed Sensing
• Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark
• Functional linear regression that's interpretable
• A unified approach to model selection and sparse recovery using regularized least squares
• Unbiased Risk Estimates for Singular Value Thresholding and Spectral Estimators
• Degrees of freedom in lasso problems
• Infinite Feature Selection: A Graph-based Feature Filtering Approach
• Adaptive piecewise polynomial estimation via trend filtering
• Sparse least trimmed squares regression for analyzing high-dimensional large data sets
• Trainable ISTA for Sparse Signal Recovery
• Linear convergence of iterative soft-thresholding
• Pleiotropy robust methods for multivariable Mendelian randomization
• Regularized Matrix Regression
• Distributed Basis Pursuit
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• Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange
• Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition
• Least angle and ${\ell }_1$ penalized regression: A review
• Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction
• Accelerated Projected Gradient Method for Linear Inverse Problems with Sparsity Constraints
• Euclid preparation: II. The EuclidEmulator -- A tool to compute the cosmology dependence of the nonlinear matter power spectrum
• Penalized Composite Quasi-Likelihood for Ultrahigh-Dimensional Variable Selection
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• Euclid preparation: IX. EuclidEmulator2 -- Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations
• On the Prediction Performance of the Lasso
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• Bayesian Variable Selection and Estimation for Group Lasso
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• Group Lasso with Overlaps: the Latent Group Lasso approach
• Lasso Screening Rules via Dual Polytope Projection
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• Dynamic Updating for L1 Minimization
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• Projective Inference in High-dimensional Problems: Prediction and Feature Selection
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• The Sliding Frank-Wolfe Algorithm and its Application to Super-Resolution Microscopy
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• Sparse Identification of Nonlinear Dynamical Systems via Reweighted ${\ell }_1$-regularized Least Squares
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• Joint variable and rank selection for parsimonious estimation of high-dimensional matrices
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• Implementing local-explainability in Gradient Boosting Trees: Feature Contribution
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• A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint
• Best subset selection, persistence in high-dimensional statistical learning and optimization under $l_1$ constraint
• A least-squares method for sparse low rank approximation of multivariate functions
• Online Feature Selection with Group Structure Analysis
• Uncertainty propagation of p-boxes using sparse polynomial chaos expansions
• Accelerating gradient projection methods for ${\ell }_1$-constrained signal recovery by steplength selection rules
• Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso
• Safe Screening With Variational Inequalities and Its Application to LASSO
• A comparative study of feature selection methods for stress hotspot classification in materials
• Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables
• A generalized sampling and preconditioning scheme for sparse approximation of polynomial chaos expansions
• Consistent selection of tuning parameters via variable selection stability
• Performance Analysis of Sparse Recovery Based on Constrained Minimal Singular Values
• On the performance of algorithms for the minimization of ${\ell }_1$-penalized functionals
• CMB data analysis and sparsity
• Anchor regression: heterogeneous data meets causality
• Sparse Recovery via Differential Inclusions
• The Pristine survey III: Spectroscopic confirmation of an efficient search for extremely metal-poor stars
• Projection-Based and Look Ahead Strategies for Atom Selection
• Complexity Analysis of the Lasso Regularization Path
• Sparse Regression: Scalable algorithms and empirical performance
• Mind the duality gap: safer rules for the Lasso
• Least Squares Approximation for a Distributed System
• Nonparametric sparsity and regularization
• Discussion: One-step sparse estimates in nonconcave penalized likelihood models
• A New High-Dimensional Time Series Approach for Wind Speed, Wind Direction and Air Pressure Forecasting
• Exhaustive search for sparse variable selection in linear regression
• A Tight Bound of Hard Thresholding
• Super-Linear Convergence of Dual Augmented-Lagrangian Algorithm for Sparsity Regularized Estimation
• Cross validation in LASSO and its acceleration
• Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels
• Penalized maximum likelihood estimation and variable selection in geostatistics
• A Note on the SPICE Method
• Statistical Significance of the Netflix Challenge
• High-Dimensional Non-Linear Variable Selection through Hierarchical Kernel Learning
• Gaussian model selection with an unknown variance
• Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID
• Analysis of Regularized LS Reconstruction and Random Matrix Ensembles in Compressed Sensing
• A Safe Screening Rule for Sparse Logistic Regression
• Penalized variable selection procedure for Cox models with semiparametric relative risk
• Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory
• Necessary and sufficient conditions of solution uniqueness in ${\ell }_1$ minimization
• Exact Post-Selection Inference for Sequential Regression Procedures
• Robust Nonparametric Regression via Sparsity Control with Application to Load Curve Data Cleansing
• A simple forward selection procedure based on false discovery rate control
• Surrogate modeling based on resampled polynomial chaos expansions
• Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models
• Improved variable selection with Forward-Lasso adaptive shrinkage
• An MDL framework for sparse coding and dictionary learning
• Homotopy based algorithms for ${\ell }_0$-regularized least-squares
• SNR-adaptive OCT angiography enabled by statistical characterization of intensity and decorrelation with multi-variate time series model
• Goodness of fit tests for high-dimensional linear models
• Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering
• High dimensional thresholded regression and shrinkage effect
• A structured matrix factorization framework for large scale calcium imaging data analysis
• Small area estimation of the homeless in Los Angeles: An application of cost-sensitive stochastic gradient boosting
• Discussion: A tale of three cousins: Lasso, L2Boosting and Dantzig
• Pairing Conceptual Modeling with Machine Learning
• Compressive Inverse Scattering I. High Frequency SIMO Measurements
• Surrogate-Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error
• Heuristic parameter-choice rules for convex variational regularization based on error estimates
• Interpretable Random Forests via Rule Extraction
• Sparse Geometric Representation Through Local Shape Probing
• Convergence Speed of a Dynamical System for Sparse Recovery
• Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems
• Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)
• On Various Confidence Intervals Post-Model-Selection
• Feature selection guided by structural information
• Bootstrap inference for network construction with an application to a breast cancer microarray study
• Adaptive ADMM with Spectral Penalty Parameter Selection
• Period Analysis using the Least Absolute Shrinkage and Selection Operator (Lasso)
• A Primal Dual Active Set Algorithm with Continuation for Compressed Sensing
• Reliability analysis of arbitrary systems based on active learning and global sensitivity analysis
• Reduced and All-at-Once Approaches for Model Calibration and Discovery in Computational Solid Mechanics
• Surrogate assisted active subspace and active subspace assisted surrogate -- A new paradigm for high dimensional structural reliability analysis
• Discussion: The Dantzig selector: Statistical estimation when $p$ is much larger than $n$
• Bolasso: model consistent Lasso estimation through the bootstrap
• Thresholded Lasso for high dimensional variable selection and statistical estimation
• Shrinkage Estimation Strategies in Generalized Ridge Regression Models Under Low/High-Dimension Regime
• Stochastic Coordinate Coding and Its Application for Drosophila Gene Expression Pattern Annotation
• Variable selection in functional regression models: a review
• Variational properties of value functions
• Consistent selection via the Lasso for high dimensional approximating regression models
• Improved machine learning algorithm for predicting ground state properties
• Learning Dictionaries with Bounded Self-Coherence
• Selective Sequential Model Selection
• Newton Sketch: A Linear-time Optimization Algorithm with Linear-Quadratic Convergence
• Spectral likelihood expansions for Bayesian inference
• First and Second Order Methods for Online Convolutional Dictionary Learning
• Robust VIF regression with application to variable selection in large data sets
• Variable selection for general index models via sliced inverse regression
• Experimental evolution of diverse Escherichia coli metabolic mutants identifies genetic loci for convergent adaptation of growth rate
• Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows
• Efficient and Modular Implicit Differentiation
• The many Shapley values for model explanation
• Regularization of Case-Specific Parameters for Robustness and Efficiency
• Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness
• A greedy approach to sparse canonical correlation analysis
• Sparse Regression with Multi-type Regularized Feature Modeling
• Sparse principal component regression with adaptive loading
• Parametric or nonparametric? A parametricness index for model selection
• Unbiased Markov chain Monte Carlo with couplings
• Stability Selection
• Predicting Growth Rate from Gene Expression
• Recursive ${\ell }_{1,\infty }$ Group lasso
• Optimizing regularized Cholesky score for order-based learning of Bayesian networks
• Sparse Recovery and Dictionary Learning from Nonlinear Compressive Measurements
• Asymptotic equivalence of regularization methods in thresholded parameter space
• Inferring sparse Gaussian graphical models with latent structure
• Sensitivity Analysis using Approximate Moment Condition Models
• Inferring large graphs using l1-penalized likelihood
• Convex and non-convex regularization methods for spatial point processes intensity estimation
• Quantum Differentially Private Sparse Regression Learning
• Smooth Lasso Estimator for the Function-on-Function Linear Regression Model
• RENT -- Repeated Elastic Net Technique for Feature Selection
• A practical illustration of the importance of realistic individualized treatment rules in causal inference
• Robust and Sparse Regression via $\gamma$-divergence
• Sparse principal component regression for generalized linear models
• Discussion: The Dantzig selector: Statistical estimation when $p$ is much larger than $n$
• Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions
• Ultrahigh dimensional variable selection: beyond the linear model
• Prediction error of cross-validated Lasso
• ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar
• An Equivalence between the Lasso and Support Vector Machines
• Bayesian inference for queueing networks and modeling of internet services
• Semi-Automated Creation of Density Functional Tight Binding Models Through Leveraging Chebyshev Polynomial-based Force Fields
• Feature Learning from Spectrograms for Assessment of Personality Traits
• Non-convex regularization in remote sensing
• Simultaneous prediction of multiple outcomes using revised stacking algorithms
• Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting
• Model selection and structure specification in ultra-high dimensional generalised semi-varying coefficient models
• Asymptotic properties for combined $L_1$ and concave regularization
• A fast and Accurate Similarity-constrained Subspace Clustering Framework for Unsupervised Hyperspectral Image Classification
• Greedy Algorithms for Sparse Reinforcement Learning
• Sparse image reconstruction for molecular imaging
• How much does your data exploration overfit? Controlling bias via information usage
• The Price of Interpretability
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• Bayesian variable selection and data integration for biological regulatory networks
• The ranking lasso and its application to sport tournaments
• Revisiting Marginal Regression
• High-dimensional influence measure
• Pac-bayesian bounds for sparse regression estimation with exponential weights
• Sparsity considerations for dependent observations
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• A study of pre-validation
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• Scalable Greedy Feature Selection via Weak Submodularity
• Robust Parametric Classification and Variable Selection by a Minimum Distance Criterion
• Sparse Recovery of Streaming Signals Using L1-Homotopy
• High-Dimensional Feature Selection for Genomic Datasets
• Supersparse Linear Integer Models for Interpretable Classification
• Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks
• Testing whether linear equations are causal: A free probability theory approach
• Covariance Eigenvector Sparsity for Compression and Denoising
• Inference in High Dimensions with the Penalized Score Test
• Sparse Coding by Spiking Neural Networks: Convergence Theory and Computational Results
• When do stepwise algorithms meet subset selection criteria?
• Regression-based sparse polynomial chaos for uncertainty quantification of subsurface flow models
• A Power and Prediction Analysis for Knockoffs with Lasso Statistics
• Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI
• Structures and Assumptions: Strategies to Harness Gene $\times$ Gene and Gene $\times$ Environment Interactions in GWAS
• Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data
• Sparse regression algorithm for activity estimation in $\gamma$ spectrometry
• Machine Learning Methods Economists Should Know About
• High-Dimensional Estimation of Structured Signals from Non-Linear Observations with General Convex Loss Functions
• Dynamic Feature Acquisition Using Denoising Autoencoders
• Compact atomic descriptors enable accurate predictions via linear models
• Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization
• An Empirical Study of ADMM for Nonconvex Problems
• On Probability of Support Recovery for Orthogonal Matching Pursuit Using Mutual Coherence
• Regularization vs. Relaxation: A conic optimization perspective of statistical variable selection
• Model selection of chaotic systems from data with hidden variables using sparse data assimilation
• A Dantzig Selector Approach to Temporal Difference Learning
• Classification of gravitational-wave glitches via dictionary learning
• Enhancing the Accuracy of Density Functional Tight Binding Models Through ChIMES Many-body Interaction Potentials
• Statistically and Computationally Efficient Change Point Localization in Regression Settings
• MM Algorithms for Minimizing Nonsmoothly Penalized Objective Functions
• A Path Algorithm for Constrained Estimation
• Chebyshev Inertial Iteration for Accelerating Fixed-Point Iterations
• Trust, but verify: benefits and pitfalls of least-squares refitting in high dimensions
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• Measuring transferability issues in machine-learning force fields: The example of Gold-Iron interactions with linearized potentials
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• Ultrahigh Dimensional Variable Selection for Mapping Soil Carbon
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• L1Packv2: A Mathematica package for minimizing an ${\ell }_1$-penalized functional
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• Unfolding overlapped slitless imaging spectrometer data for extended sources
• Sparse Range-constrained Learning and Its Application for Medical Image Grading
• Solution Path Clustering with Adaptive Concave Penalty
• A Fast Dictionary Learning Method for Coupled Feature Space Learning
• Sparse Bilinear Logistic Regression
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• Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark
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• Asymptotic Analysis of LASSOs Solution Path with Implications for Approximate Message Passing
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• Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables
• Stochastic identification of malware with dynamic traces
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• Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition
• On Improved Loss Estimation for Shrinkage Estimators
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• Model-Consistent Sparse Estimation through the Bootstrap
• Regularization with the Smooth-Lasso procedure
• Proof of Convergence and Performance Analysis for Sparse Recovery via Zero-point Attracting Projection
• Hybrid safe-strong rules for efficient optimization in lasso-type problems
• More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming
• Fault-Tolerant Control of Linear Quantum Stochastic Systems
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• Analytic Continued Fractions for Regression: A Memetic Algorithm Approach
• Impacts of high dimensionality in finite samples
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• Handling cellwise outliers by sparse regression and robust covariance
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• An Extended Newton-type Algorithm for ${\ell }_2$-Regularized Sparse Logistic Regression and Its Efficiency for Classifying Large-scale Datasets
• Monte Carlo Simulation for Lasso-Type Problems by Estimator Augmentation
• Nonsmooth Implicit Differentiation for Machine Learning and Optimization
• Robust online active learning
• High-dimensional Linear Regression for Dependent Data with Applications to Nowcasting
• Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition
• Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models
• Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition
• Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data
• Sparse structures with LASSO through Principal Components: forecasting GDP components in the short-run
• Group sparse optimization via ${\ell }_{p,q}$ regularization
• Sparse regulatory networks
• A ROAD to Classification in High Dimensional Space
• A Risk Ratio Comparison of $l_0$ and $l_1$ Penalized Regression
• The Reciprocal Bayesian LASSO
• Efficient computation and analysis of distributional Shapley values
• Compressed sensing in scanning tunneling microscopy/spectroscopy for observation of quasi-particle interference
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• Selective machine learning of doubly robust functionals
• Polynomial Chaos Expansion method as a tool to evaluate and quantify field homogeneities of a novel waveguide RF Wien Filter
• Direct estimation of density functionals using a polynomial basis
• A General Framework for Portfolio Construction Based on Generative Models of Asset Returns
• GP-RVM: Genetic Programing-based Symbolic Regression Using Relevance Vector Machine
• Rejoinder: One-step sparse estimates in nonconcave penalized likelihood models
• SEK: Sparsity exploiting $k$-mer-based estimation of bacterial community composition
• Learning to serve: an experimental study for a new learning from demonstrations framework
• Detecting Multiple Change Points Using Adaptive Regression Splines with Application to Neural Recordings
• Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series
• mplot: An R Package for Graphical Model Stability and Variable Selection Procedures
• Linearly Supporting Feature Extraction For Automated Estimation Of Stellar Atmospheric Parameters
• Deep Sparse Coding Using Optimized Linear Expansion of Thresholds
• Sequential Selection Procedures and False Discovery Rate Control
• Combined l_1 and greedy l_0 penalized least squares for linear model selection
• Uniformly Valid Confidence Sets Based on the Lasso
• On the energy landscape of deep networks
• Estimating Unknown Sparsity in Compressed Sensing
• Multiple Change-point Detection: a Selective Overview
• Bayesian generalized fused lasso modeling via NEG distribution
• Bayesian $l_0$-regularized Least Squares
• Data segmentation algorithms: Univariate mean change and beyond
• Contextual Outlier Interpretation
• Learning networks from high dimensional binary data: An application to genomic instability data
• A statistical mechanics approach to de-biasing and uncertainty estimation in LASSO for random measurements
• Don't relax: early stopping for convex regularization
• The Trimmed Lasso: Sparsity and Robustness
• Sparse Approximation via Penalty Decomposition Methods
• Health improvement framework for planning actionable treatment process using surrogate Bayesian model
• A projection pursuit framework for testing general high-dimensional hypothesis
• Seeded PageRank Solution Paths
• On the Sensitivity of the Lasso to the Number of Predictor Variables
• Robust subset selection
• Path Following and Empirical Bayes Model Selection for Sparse Regression
• Bayesian Structured Sparsity from Gaussian Fields
• Learning with tree-based tensor formats
• Compressive Wideband Spectrum Sensing for Fixed Frequency Spectrum Allocation
• Greedy algorithms for prediction
• Reluctant Interaction Modeling
• Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion
• Parametric Programming Approach for More Powerful and General Lasso Selective Inference
• Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
• A cost-sensitive constrained Lasso
• Online matrix factorization for Markovian data and applications to Network Dictionary Learning
• When does OMP achieve exact recovery with continuous dictionaries?
• Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods
• Structured variable selection in support vector machines
• Mantis: Predicting System Performance through Program Analysis and Modeling
• Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption
• Variational approximation for heteroscedastic linear models and matching pursuit algorithms
• Gap Safe screening rules for sparsity enforcing penalties
• Variable selection and estimation for semi-parametric multiple-index models
• High-dimensional variable selection via low-dimensional adaptive learning
• Convex Relaxations for Subset Selection
• Kernel Partial Correlation Coefficient -- a Measure of Conditional Dependence
• Fourier Analysis-based Iterative Combinatorial Auctions
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• A path algorithm for the Fused Lasso Signal Approximator
• Bayesian Synthesis: Combining subjective analyses, with an application to ozone data
• Suboptimal Solution Path Algorithm for Support Vector Machine
• Oracle-order Recovery Performance of Greedy Pursuits with Replacement against General Perturbations
• A Sparse Sampling-based framework for Semantic Fast-Forward of First-Person Videos
• On the Distribution of Penalized Maximum Likelihood Estimators: The LASSO, SCAD, and Thresholding
• An l1-Oracle Inequality for the Lasso
• A stochastic algorithm for probabilistic independent component analysis
• Model-assisted estimation in high-dimensional settings for survey data
• A D.C. Programming Approach to the Sparse Generalized Eigenvalue Problem
• Characterizing $L_2$Boosting
• The Backbone Method for Ultra-High Dimensional Sparse Machine Learning
• Interpretable Sparse Proximate Factors for Large Dimensions
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• Accelerating cross-validation with total variation and its application to super-resolution imaging
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• On estimating covariances between many assets with histories of highly variable length
• Proximal Markov chain Monte Carlo algorithms
• General Convolutional Sparse Coding with Unknown Noise
• Two-stage Sampling, Prediction and Adaptive Regression via Correlation Screening (SPARCS)
• Multivariate convex regression with adaptive partitioning
• Generic Error Bounds for the Generalized Lasso with Sub-Exponential Data
• Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory
• Weighted Meta-Learning
• Interpretability with Accurate Small Models
• Degrees of freedom for nonlinear least squares estimation
• Inference for feature selection using the Lasso with high-dimensional data
• The restricted consistency property of leave-$n_v$-out cross-validation for high-dimensional variable selection
• Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
• Robust Bayesian Regression with Synthetic Posterior
• Cross-Validation with Confidence
• Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines
• Efficient Forward Architecture Search
• Supersparse Linear Integer Models for Predictive Scoring Systems
• Hyper-sparse optimal aggregation
• Compressed sensing radar detectors under the row-orthogonal design model: a statistical mechanics perspective
• A study on tuning parameter selection for the high-dimensional lasso
• Sparse Index Tracking Based On $L_{1/2}$ Model And Algorithm
• Surrogate modelling for stochastic dynamical systems by combining NARX models and polynomial chaos expansions
• SURE Information Criteria for Large Covariance Matrix Estimation and Their Asymptotic Properties
• Faithful Multimodal Explanation for Visual Question Answering
• Channel Protection: Random Coding Meets Sparse Channels
• Online Learning for Matrix Factorization and Sparse Coding
• Dictionary Learning for Robotic Grasp Recognition and Detection
• Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals
• Flexible covariate-adjusted exact tests of randomized treatment effects with application to a trial of HIV education
• Algorithms for Fitting the Constrained Lasso
• Some Two-Step Procedures for Variable Selection in High-Dimensional Linear Regression
• The Stochastic Properties of ${\ell }^1$-Regularized Spherical Gaussian Fields
• A Scalable Empirical Bayes Approach to Variable Selection in Generalized Linear Models
• Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions
• Classification of arrayCGH data using a fused SVM
• Sparse Popularity Adjusted Stochastic Block Model
• A Combinatorial Algorithm to Compute Regularization Paths
• Learning the kernel matrix via predictive low-rank approximations
• Parallel integrative learning for large-scale multi-response regression with incomplete outcomes
• The Lasso under Heteroscedasticity
• A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSO
• Sparse Polynomial Chaos Expansion for Universal Stochastic Kriging
• A Survey of Tuning Parameter Selection for High-dimensional Regression
• Power Module Heat Sink Design Optimization with Ensembles of Data-Driven Polynomial Chaos Surrogate Models
• Compressed Support Vector Machines
• A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems
• Group Variable Selection via a Hierarchical Lasso and Its Oracle Property
• Photoacoustic monitoring of blood oxygenation during neurosurgical interventions
• An efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting
• Deep neural network initialization with decision trees
• Generalized Kalman Smoothing: Modeling and Algorithms
• Safe Grid Search with Optimal Complexity
• Strong rules for nonconvex penalties and their implications for efficient algorithms in high-dimensional regression
• Surrogate modeling of indoor down-link human exposure based on sparse polynomial chaos expansion
• Horseshoe Regularization for Feature Subset Selection
• Sparse principal component regression via singular value decomposition approach
• AIC for Non-concave Penalized Likelihood Method
• Regression modeling on stratified data with the lasso
• The Learning and Prediction of Application-level Traffic Data in Cellular Networks
• Fast and Accurate Algorithms for Re-Weighted L1-Norm Minimization
• The matryoshka doll prior: principled multiplicity correction in Bayesian model comparison
• Dirichlet-Laplace priors for optimal shrinkage
• Functional Group Bridge for Simultaneous Regression and Support Estimation
• Risk of estimators for Sobol' sensitivity indices based on metamodels
• LASSO, Iterative Feature Selection and the Correlation Selector: Oracle Inequalities and Numerical Performances
• Compressive sensing: a paradigm shift in signal processing
• Sequential Lasso for feature selection with ultra-high dimensional feature space
• Total Variation Minimization Based Compressive Wideband Spectrum Sensing for Cognitive Radios
• Ensemble Sparse Models for Image Analysis
• The Statistics of Streaming Sparse Regression
• Joint estimation of $K$ related regression models with simple $L_1$-norm penalties
• Prediction error after model search
• Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data
• Regularization Approach for Network Modeling of German Power Derivative Market
• Surrogate modeling with functional nonlinear autoregressive models (F-NARX)
• Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work
• GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations
• A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition
• The Efficient Shrinkage Path: Maximum Likelihood of Minimum MSE Risk
• Sparse online variational Bayesian regression
• Ideal formulations for constrained convex optimization problems with indicator variables
• Flow-based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance
• A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model
• Big Data Analysis Using Shrinkage Strategies
• Robust and Parallel Bayesian Model Selection
• Iterative Hard Thresholding for Model Selection in Genome-Wide Association Studies
• New developments in Sparse PLS regression
• CoCoLasso for High-dimensional Error-in-variables Regression
• Delete or merge regressors for linear model selection
• Natural coordinate descent algorithm for L1-penalised regression in generalised linear models
• An Efficient Two-Stage Sparse Representation Method
• The geometry of least squares in the 21st century
• Solving OSCAR regularization problems by proximal splitting algorithms
• Asymptotic Properties of Lasso+mLS and Lasso+Ridge in Sparse High-dimensional Linear Regression
• Conditional Gradient Algorithms for Rank-One Matrix Approximations with a Sparsity Constraint
• The Loss Rank Criterion for Variable Selection in Linear Regression Analysis
• Compressive Wave Computation
• Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) - The ${\ell }_0$ Method
• Dictionary Learning by Dynamical Neural Networks
• Bridging between soft and hard thresholding by scaling
• Regularization Path of Cross-Validation Error Lower Bounds
• Forward-Selected Panel Data Approach for Program Evaluation
• Quantum Algorithms for the Pathwise Lasso
• Non-intrusive reduced order models for partitioned fluid-structure interactions
• Bayes Regularized Graphical Model Estimation in High Dimensions
• Local Linear Convergence of ISTA and FISTA on the LASSO Problem
• Graphical continuous Lyapunov models
• Fast Marginal Likelihood Estimation of the Ridge Parameter(s) in Ridge Regression and Generalized Ridge Regression for Big Data
• Scalable Bayesian Variable Selection for Structured High-dimensional Data
• Automatic monotonicity detection for Gaussian Processes
• Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis
• On the ${\ell }_1-{\ell }_q$ Regularized Regression
• Uncoupled Regression from Pairwise Comparison Data
• Adaptive estimation of the baseline hazard function in the Cox model by model selection, with high-dimensional covariates
• Variable selection through CART
• Re-scale boosting for regression and classification
• Sparse approximations of protein structure from noisy random projections
• HAMLET: A Hierarchical Agent-based Machine Learning Platform
• Covariate Balancing Based on Kernel Density Estimates for Controlled Experiments
• NESVM: a Fast Gradient Method for Support Vector Machines
• Solving FDR-Controlled Sparse Regression Problems with Five Million Variables on a Laptop
• Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression
• Machine learning reveals features of spinon Fermi surface
• Zoetrope Genetic Programming for Regression
• Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution
• Parameter Estimation with the Ordered ${\ell }_2$ Regularization via an Alternating Direction Method of Multipliers
• Convolutional sparse coding for capturing high speed video content
• Wasserstein variational gradient descent: From semi-discrete optimal transport to ensemble variational inference
• Selective Image Super-Resolution
• Group-bound: confidence intervals for groups of variables in sparse high-dimensional regression without assumptions on the design
• Regression shrinkage and grouping of highly correlated predictors with HORSES
• Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation
• Convex vs nonconvex approaches for sparse estimation: GLasso, Multiple Kernel Learning and Hyperparameter GLasso
• Bias-Aware Inference in Regularized Regression Models
• PARNES: A rapidly convergent algorithm for accurate recovery of sparse and approximately sparse signals
• MLF-SC: Incorporating multi-layer features to sparse coding for anomaly detection
• Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation
• Cross validation in sparse linear regression with piecewise continuous nonconvex penalties and its acceleration
• AcSel: selecting variables with accuracy in correlated datasets
• High-dimensional stochastic optimization with the generalized Dantzig estimator
• Statistical Mechanics of Dynamical System Identification
• A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network
• Unbalanced Optimal Transport through Non-negative Penalized Linear Regression
• Outlier Detection Using Nonconvex Penalized Regression
• High dimensional regression and matrix estimation without tuning parameters
• The Smooth-Lasso and other ${\ell }_1+{\ell }_2$-penalized methods
• Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension
• A greedy anytime algorithm for sparse PCA
• Semiparametric Sparse Discriminant Analysis
• A Privacy-Preserving Federated Learning Approach for Kernel methods
• Penalized Sparse Covariance Regression with High Dimensional Covariates
• Mixture Components Inference for Sparse Regression: Introduction and Application for Estimation of Neuronal Signal from fMRI BOLD
• selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets
• Improving Vehicle Re-Identification using CNN Latent Spaces: Metrics Comparison and Track-to-track Extension
• Applying Polynomial Chaos Expansion to Assess Probabilistic Available Delivery Capability for Distribution Networks with Renewables
• Toward Real-Time Image Annotation Using Marginalized Coupled Dictionary Learning
• A Survey Of Regression Algorithms And Connections With Deep Learning
• Multimodal Sparse Bayesian Dictionary Learning
• Local and Global Convergence of an Inertial Version of Forward-Backward Splitting
• Nonregular and Minimax Estimation of Individualized Thresholds in High Dimension with Binary Responses
• Comparisons of penalized least squares methods by simulations
• Discussion: "A significance test for the lasso"
• Evaluation of Generalized Degrees of Freedom for Sparse Estimation by Replica Method
• More Powerful and General Selective Inference for Stepwise Feature Selection using the Homotopy Continuation Approach
• Learning step sizes for unfolded sparse coding
• Penalized Poisson model for network meta-analysis of individual patient time-to-event data
• Average Case Analysis of Multichannel Sparse Recovery Using Convex Relaxation
• Hamming Compressed Sensing
• PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection
• Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning
• Autoencoder based Domain Adaptation for Speaker Recognition under Insufficient Channel Information
• Post-selection inference with HSIC-Lasso
• Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix
• Data-Driven Malaria Prevalence Prediction in Large Densely-Populated Urban Holoendemic sub-Saharan West Africa: Harnessing Machine Learning Approaches and 22-years of Prospectively Collected Data
• MM for Penalized Estimation
• OEM for least squares problems
• On Bias and Its Reduction via Standardization in Discretized Electromagnetic Source Localization Problems
• MIP-BOOST: Efficient and Effective $L_0$ Feature Selection for Linear Regression
• Best subset selection is robust against design dependence
• Imaging with highly incomplete and corrupted data
• Hyperspectral recovery from RGB images using Gaussian Processes
• A multivariate adaptive stochastic search method for dimensionality reduction in classification
• ADMM-MCP Framework for Sparse Recovery with Global Convergence
• A note relating ridge regression and OLS p-values to preconditioned sparse penalized regression
• Predictor-dependent shrinkage for linear regression via partial factor modeling
• A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization
• N${}^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data
• Leveraging Sparse Linear Layers for Debuggable Deep Networks
• Unsupervised parameter selection for denoising with the elastic net
• Functional Brain Networks Discovery Using Dictionary Learning with Correlated Sparsity
• High-dimensional additive hazard models and the Lasso
• Model Selection in Undirected Graphical Models with the Elastic Net
• Sparse Isotropic Regularization for Spherical Harmonic Representations of Random Fields on the Sphere
• Non-bifurcating phylogenetic tree inference via the adaptive LASSO
• Uncovering Coresets for Classification With Multi-Objective Evolutionary Algorithms
• Polynomial-Chaos-based Kriging
• Joint segmentation of many aCGH profiles using fast group LARS
• Time-dependent global sensitivity analysis of the Doyle-Fuller-Newman model
• A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients
• Sapphire: Automatic Configuration Recommendation for Distributed Storage Systems
• From Predictions to Decisions: Using Lookahead Regularization
• Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learning
• Model Selection for High Dimensional Quadratic Regression via Regularization
• On the Distribution of the Adaptive LASSO Estimator
• Metrics for Multivariate Dictionaries
• VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising
• A Lasso-OLS Hybrid Approach to Covariate Selection and Average Treatment Effect Estimation for Clustered RCTs Using Design-Based Methods
• The Strong Screening Rule for SLOPE
• Modelling Interactions in High-dimensional Data with Backtracking
• Penalized Interaction Estimation for Ultrahigh Dimensional Quadratic Regression
• An Algorithm for Nonlinear, Nonparametric Model Choice and Prediction
• Scaled minimax optimality in high-dimensional linear regression: A non-convex algorithmic regularization approach
• Estimating the Rate Constant from Biosensor Data via an Adaptive Variational Bayesian Approach
• A Study on Unsupervised Dictionary Learning and Feature Encoding for Action Classification
• Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation
• Likelihood Adaptively Modified Penalties
• Efficient Computation of Sparse and Robust Maximum Association Estimators
• Sparse polynomial chaos expansions of frequency response functions using stochastic frequency transformation
• Sparse model from optimal nonuniform embedding of time series
• An Exploratory Analysis of Biased Learners in Soft-Sensing Frames
• Performance evaluation of matrix factorization for fMRI data
• Optimal Explanations of Linear Models
• Sparse Unit-Sum Regression
• Data Sharing and Resampled LASSO: A word based sentiment Analysis for IMDb data
• Decentralized Dynamic Discriminative Dictionary Learning
• Fast construction of efficient composite likelihood equations
• Sparse bottleneck neural networks for exploratory non-linear visualization of Patch-seq data
• A Tutorial on Libra: R package for the Linearized Bregman Algorithm in High Dimensional Statistics
• Bayesian Lasso Posterior Sampling via Parallelized Measure Transport
• Multiple testing via relative belief ratios
• mNARX+: A surrogate model for complex dynamical systems using manifold-NARX and automatic feature selection
• Efficient Point-to-Subspace Query in ${\ell }^1$ with Application to Robust Object Instance Recognition
• Sparse Factorization Layers for Neural Networks with Limited Supervision
• A Benson-Type Algorithm for Bounded Convex Vector Optimization Problems with Vertex Selection
• Bayesian Variable Selection for Skewed Heteroscedastic Response
• SimpleTrack:Adaptive Trajectory Compression with Deterministic Projection Matrix for Mobile Sensor Networks
• Feature selection using nearest attributes
• A Generic Path Algorithm for Regularized Statistical Estimation
• Aggregation of supports along the Lasso path
• A General Framework for Fast Stagewise Algorithms
• Regularization Methods Based on the $L_q$-Likelihood for Linear Models with Heavy-Tailed Errors
• Graph Classification using Signal-Subgraphs: Applications in Statistical Connectomics
• Model Selection Consistency for Cointegrating Regressions
• An adaptive shortest-solution guided decimation approach to sparse high-dimensional linear regression
• Sharp Support Recovery from Noisy Random Measurements by L1 minimization
• Want Answers? A Reddit Inspired Study on How to Pose Questions
• A sparse regulatory network of copy-number driven expression reveals putative breast cancer oncogenes
• Keeping greed good: sparse regression under design uncertainty with application to biomass characterization
• Optimal ETF Selection for Passive Investing
• Regularization and Bayesian Learning in Dynamical Systems: Past, Present and Future
• Penalized Regression Models for the NBA
• Adaptive LASSO-type estimation for ergodic diffusion processes
• Uncertainty quantification of a thrombosis model considering the clotting assay PFA-100
• Graph selection with GGMselect
• OMP-type Algorithm with Structured Sparsity Patterns for Multipath Radar Signals
• SNAP: A semismooth Newton algorithm for pathwise optimization with optimal local convergence rate and oracle properties
• LASSO-Patternsearch algorithm with application to ophthalmology and genomic data
• Nonlinear Estimators and Tail Bounds for Dimension Reduction in $l_1$ Using Cauchy Random Projections
• Economic variable selection
• A Method Expanding 2 by 2 Contingency Table by Obtaining Tendencies of Boolean Operators: Boolean Monte Carlo Method
• A dual Newton based preconditioned proximal point algorithm for exclusive lasso models
• Quantum circuit-like learning: A fast and scalable classical machine-learning algorithm with similar performance to quantum circuit learning
• Parallel and Communication Avoiding Least Angle Regression
• Improved Search Strategies with Application to Estimating Facial Blendshape Parameters
• Enabling SQL-based Training Data Debugging for Federated Learning
• Randomized Functional Sparse Tucker Tensor for Compression and Fast Visualization of Scientific Data
• Testing for Heteroscedasticity in High-dimensional Regressions
• On Learning Continuous Pairwise Markov Random Fields
• A Brief Survey of Associations Between Meta-Learning and General AI
• Credit scoring using neural networks and SURE posterior probability calibration
• Divide-and-conquer methods for big data analysis
• Use Of Vapnik-Chervonenkis Dimension in Model Selection
• Computing Estimators of Dantzig Selector type via Column and Constraint Generation
• Coupled Depth Learning
• It Is Likely That Your Loss Should be a Likelihood
• Valid uncertainty quantification about the model in a linear regression setting
• Graph Model Selection via Random Walks
• Deconfounding and Causal Regularization for Stability and External Validity
• Variable selection for Gaussian process regression through a sparse projection
• Screening Rules and its Complexity for Active Set Identification
• Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression
• Supervised Deep Sparse Coding Networks
• Contextual Local Explanation for Black Box Classifiers
• Penalized robust estimators in logistic regression with applications to sparse models
• Replication-based emulation of the response distribution of stochastic simulators using generalized lambda distributions
• Penalized-likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding
• Sparse Sliced Inverse Regression Via Lasso
• The Well Tempered Lasso
• Subsampling Winner Algorithm for Feature Selection in Large Regression Data
• A latent variable model for survival time prediction with censoring and diverse covariates
• Sparse partial least squares for on-line variable selection in multivariate data streams
• Sparse Coding Approach for Multi-Frame Image Super Resolution
• A generalised OMP algorithm for feature selection with application to gene expression data
• Submodularity in Statistics: Comparing the Success of Model Selection Methods
• Transfer Learning Using Feature Selection
• Adaptive elastic net and Separate Selection from Least Squares for ultra-high dimensional regression models
• Selective Inference and Learning Mixed Graphical Models
• Reducing bias and alleviating the influence of excess of zeros with multioutcome adaptive LAD-lasso
• Gene-Environment Interaction: A Variable Selection Perspective
• Dictionary and Image Recovery from Incomplete and Random Measurements
• Learning Quadrangulated Patches For 3D Shape Processing
• Safe Feature Pruning for Sparse High-Order Interaction Models
• Adaptive Elastic Net Method for Cox Model
• A Conversation with Jerry Friedman
• Learning Dynamic Feature Selection for Fast Sequential Prediction
• Anti-sparse coding for approximate nearest neighbor search
• EEG source localization using a sparsity prior based on Brodmann areas
• Nonparametric Variable Screening with Optimal Decision Stumps
• Variable Selection for Survival Data with A Class of Adaptive Elastic Net Techniques
• Local Linear Regression on Manifolds and its Geometric Interpretation
• Generalized Fiducial Inference for Ultrahigh Dimensional Regression
• Maximum Margin Principal Components
• Online Graph Topology Learning from Matrix-valued Time Series
• Fully Bayesian Penalized Regression with a Generalized Bridge Prior
• Familywise Error Rate Control via Knockoffs
• A Unified Dynamic Approach to Sparse Model Selection
• Spatial-Aware Dictionary Learning for Hyperspectral Image Classification
• Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
• CoSeNet: A Novel Approach for Optimal Segmentation of Correlation Matrices
• High-dimensional Fused Lasso Regression using Majorization-Minimization and Parallel Processing
• Dual Lasso Selector
• Nonparametric Estimation of Isotropic Covariance Function
• Compressive Conjugate Directions: Linear Theory
• SVEMnet: An R package for Self-Validated Elastic-Net Ensembles and Multi-Response Optimization in Small-Sample Mixture-Process Experiments
• TR01: Time-continuous Sparse Imputation
• ROS Regression: Integrating Regularization and Optimal Scaling Regression
• Seeds Cleansing CNMF for Spatiotemporal Neural Signals Extraction of Miniscope Imaging Data
• Confidence-Constrained Maximum Entropy Framework for Learning from Multi-Instance Data
• Bayesian Inference with the l1-ball Prior: Solving Combinatorial Problems with Exact Zeros
• The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control
• Fast Robust Kernel Regression through Sign Gradient Descent with Early Stopping
• Using the lasso method for space-time short-term wind speed predictions
• Lasso formulation of the shortest path problem
• Scalable Data-Driven Basis Selection for Linear Machine Learning Interatomic Potentials
• A robust Bayesian analysis of variable selection under prior ignorance
• Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems
• Distinguishing pairwise and higher-order interactions in coupled oscillators from time series
• Generalized Sparse Covariance-based Estimation
• A code for two-dimensional frequency analysis using the Least Absolute Shrinkage and Selection Operator (Lasso) for multidisciplinary use
• Learning low dimensional word based linear classifiers using Data Shared Adaptive Bootstrap Aggregated Lasso with application to IMDb data
• Meta Clustering for Collaborative Learning
• Hierarchical sparsity priors for regression models
• Robust Variable Selection Criteria for the Penalized Regression
• LASSO ISOtone for High Dimensional Additive Isotonic Regression
• An empirical comparative study of approximate methods for binary graphical models; application to the search of associations among causes of death in French death certificates
• A Scale and Rotational Invariant Key-point Detector based on Sparse Coding
• How Many Iterations are Sufficient for Semiparametric Estimation?
• Multilevel Surrogate-based Control Variates
• The unified maximum a posteriori (MAP) framework for neuronal system identification
• Learned Interpretable Residual Extragradient ISTA for Sparse Coding
• On the connections between algorithmic regularization and penalization for convex losses
• Streaming regularization parameter selection via stochastic gradient descent
• Penalty, Shrinkage, and Preliminary Test Estimators under Full Model Hypothesis
• MFL_COVID19: Quantifying Country-based Factors affecting Case Fatality Rate in Early Phase of COVID-19 Epidemic via Regularised Multi-task Feature Learning
• Discussion of: A statistical analysis of multiple temperature proxies: Are reconstructions of surface temperatures over the last 1000 years reliable?
• An algorithmic view of ${\ell }_2$ regularization and some path-following algorithms
• Lockout: Sparse Regularization of Neural Networks
• Reliability analysis of high-dimensional models using low-rank tensor approximations
• Lass-0: sparse non-convex regression by local search
• Projection Algorithms for Non-Convex Minimization with Application to Sparse Principal Component Analysis
• Thresholding-based Iterative Selection Procedures for Model Selection and Shrinkage
• Sorted Concave Penalized Regression
• Representing Data by a Mixture of Activated Simplices
• Data augmentation for non-Gaussian regression models using variance-mean mixtures
• A Semi-Smooth Newton Algorithm for High-Dimensional Nonconvex Sparse Learning
• Partial Correlation Estimation by Joint Sparse Regression Models
• Matrix-wise ${\ell }_0$-constrained Sparse Nonnegative Least Squares
• Two-sample testing of high-dimensional linear regression coefficients via complementary sketching
• Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding
• Dynamic Large Spatial Covariance Matrix Estimation in Application to Semiparametric Model Construction via Variable Clustering: the SCE approach
• Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression
• Modern GPR Target Recognition Methods
• Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent
• Uplift Regression: The R Package tools4uplift
• Strategic Bayesian Asset Allocation
• Recent Developments in Nonregular Fractional Factorial Designs
• Generalized Sobol sensitivity indices for dependent variables: numerical methods
• An Optimization Framework with Flexible Inexact Inner Iterations for Nonconvex and Nonsmooth Programming
• A Scalable Empirical Bayes Approach to Variable Selection
• Sparse Trace Norm Regularization
• Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees
• Fusing heterogeneous data sets
• HMLasso: Lasso with High Missing Rate
• Dictionary Learning Phase Retrieval from Noisy Diffraction Patterns
• Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms
• An Efficient Interval Uncertainty Optimization Approach Based on Quasi-sparse Response Surface
• Block based refitting in ${\ell }_{12}$ sparse regularisation
• Bayesian sparse convex clustering via global-local shrinkage priors
• Holonomic extended least angle regression
• Spatial Variable Selection and An Application to Virginia Lyme Disease Emergence
• Surrogate-based global sensitivity analysis for turbulence and fire-spotting effects in regional-scale wildland fire modeling
• Estimating Historical Functional Linear Models with a Nested Group Bridge Approach
• Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control
• On an improvement of LASSO by scaling
• Gaussian Graphical Model exploration and selection in high dimension low sample size setting
• Large-P Variable Selection in Two-Stage Models
• Safe Active Feature Selection for Sparse Learning
• Classification of abrupt changes along viewing profiles of scientific articles
• Forward Amortized Inference for Likelihood-Free Variational Marginalization
• Bayesian method for inferring the impact of geographical distance on intensity of communication
• Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing
• An Integrated Inverse Space Sparse Representation Framework for Tumor Classification
• Bayesian Lasso : Concentration and MCMC Diagnosis
• MCMC-Interactive Variational Inference
• Data-driven Regularized Inference Privacy
• An Improved Online Penalty Parameter Selection Procedure for ${\ell }_1$-Penalized Autoregressive with Exogenous Variables
• Applied Machine Learning to Predict Stress Hotspots I: Face Centered Cubic Materials
• Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
• Boosting with Structural Sparsity: A Differential Inclusion Approach
• Nonconvex Regularized Gradient Projection Sparse Reconstruction for Massive MIMO Channel Estimation
• Minimization over the l1-ball using an active-set non-monotone projected gradient
• A comprehensive guideline for regularization-path variable selection in high-dimensional Gaussian linear regression
• Prior Intensified Information Criterion
• Exact adaptive confidence intervals for linear regression coefficients
• Global sensitivity analysis of natural convection in porous enclosure: Effect of thermal dispersion, anisotropic permeability and heterogeneity
• Scalable simultaneous inference in high-dimensional linear regression models
• On the Limit Imbalanced Logistic Regression by Binary Predictors
• An SDP Relaxation for the Sparse Integer Least Squares Problem
• Projection Sparse Principal Component Analysis: an efficient least squares method
• Cox reduction and confidence sets of models: a theoretical elucidation
• Model Confidence Bounds for Variable Selection
• Dictionary-based model reduction for state estimation
• Adaptive regularization for Lasso models in the context of non-stationary data streams
• Logistic push: a regression framework for partial AUC optimization
• Global sensitivity analysis using low-rank tensor approximations
• Nonlinear Mixed-effects Scalar-on-function Models and Variable Selection for Kinematic Upper Limb Movement Data
• A Computationally Efficient Approach to False Discovery Rate Control and Power Maximisation via Randomisation and Mirror Statistic
• Assessment of Case Influence in the Lasso with a Case-weight Adjusted Solution Path
• Optimal Estimation of Slope Vector in High-dimensional Linear Transformation Model
• Greedy Criterion in Orthogonal Greedy Learning
• On the Consistency of the Bias Correction Term of the AIC for the Non-Concave Penalized Likelihood Method
• Fast Detection of Block Boundaries in Block Wise Constant Matrices: An Application to HiC data
• A sampling method based on highest density regions: Applications to surrogate models
• Convex Optimization For Non-Convex Problems via Column Generation
• Multivariate Uncertainty Quantification with Tomographic Quantile Forests
• Using a neural network approach and starspots dependent models to predict effective temperatures and ages of young stars
• A Dirichlet Process Functional Approach to Heteroscedastic-Consistent Covariance Estimation
• Convex Optimization for Linear Query Processing under Approximate Differential Privacy
• Adaptive scaling for soft-thresholding estimator
• Estimator selection with respect to Hellinger-type risks
• Sparse Models for Machine Learning
• Successive Ray Refinement and Its Application to Coordinate Descent for LASSO
• MCMC convergence diagnosis using geometry of Bayesian LASSO
• Efficient and robust high-dimensional sparse logistic regression via nonlinear primal-dual hybrid gradient algorithms
• Conformal Bayesian Computation
• Predictive multiview embedding
• A proximal-proximal majorization-minimization algorithm for nonconvex tuning-free robust regression problems
• A provable two-stage algorithm for penalized hazards regression
• Robust Elastic Net Regression
• Recovering lost and absent information in temporal networks
• Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee
• Elastic regularization in restricted Boltzmann machines: Dealing with $p\gg N$
• Estimation of sparse linear dynamic networks using the stable spline horseshoe prior
• A New Bayesian Optimization Algorithm for Complex High-Dimensional Disease Epidemic Systems
• Tom: Leveraging trend of the observed gradients for faster convergence
• l1-norm Penalized Orthogonal Forward Regression
• yaglm: a Python package for fitting and tuning generalized linear models that supports structured, adaptive and non-convex penalties
• Greedy methods, randomization approaches and multi-arm bandit algorithms for efficient sparsity-constrained optimization
• Elastic Net Procedure for Partially Linear Models
• Sparse linear regression -- CLuP achieves the ideal \emph{exact} ML
• A Partially Collapsed Sampler for Unsupervised Nonnegative Spike Train Restoration
• Efficient ${\ell }_q$ Minimization Algorithms for Compressive Sensing Based on Proximity Operator
• KNIFE: Kernel Iterative Feature Extraction
• Adaptive posterior convergence in sparse high dimensional clipped generalized linear models
• A Two-Stage Variable Selection Approach for Correlated High Dimensional Predictors
• Efficient Experimental Design for Regularized Linear Models
• On debiasing restoration algorithms: applications to total-variation and nonlocal-means
• Finding Dantzig selectors with a proximity operator based fixed-point algorithm
• Using DFTB to Model Photocatalytic Anatase-Rutile TiO${}_2$ Nanocrystalline Interfaces and their Band Alignment
• Robust selection of predictors and conditional outlier detection in a perturbed large-dimensional regression context
• Safe Screening Rules for ${\ell }_0$-Regression
• A SAEM Algorithm for Fused Lasso Penalized Non Linear Mixed Effect Models: Application to Group Comparison in Pharmacokinetic
• Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods
• Accelerated Sparse Bayesian Learning via Screening Test and Its Applications
• A Simple Homotopy Proximal Mapping for Compressive Sensing
• An Easy-to-Implement Hierarchical Standardization for Variable Selection Under Strong Heredity Constraint
• piCholesky: Polynomial Interpolation of Multiple Cholesky Factors for Efficient Approximate Cross-Validation
• Variable Selection Incorporating Prior Constraint Information into Lasso
• Computing the degrees of freedom of rank-regularized estimators and cousins
• Disjunct Support Spike and Slab Priors for Variable Selection in Regression under Quasi-sparseness
• Conditional variable screening via ordinary least squares projection
• Better Solution Principle: A Facet of Concordance between Optimization and Statistics
• Best-first Search Algorithm for Non-convex Sparse Minimization
• Lasso and equivalent quadratic penalized models
• Topic-aware chatbot using Recurrent Neural Networks and Nonnegative Matrix Factorization
• Lasso Regression: Estimation and Shrinkage via Limit of Gibbs Sampling
• Unseen Face Presentation Attack Detection Using Class-Specific Sparse One-Class Multiple Kernel Fusion Regression
• Image denoising via K-SVD with primal-dual active set algorithm
• An Algorithmic Theory of Dependent Regularizers, Part 1: Submodular Structure
• Optimization of Structural Similarity in Mathematical Imaging
• Sparse and Efficient Estimation for Partial Spline Models with Increasing Dimension
• Robust Compressed Sensing and Sparse Coding with the Difference Map
• The Maximum Entropy Relaxation Path
• Primal path algorithm for compositional data analysis
• Iterated Feature Screening based on Distance Correlation for Ultrahigh-Dimensional Censored Data with Covariates Measurement Error
• L2 Boosting on generalized Hoeffding decomposition for dependent variables. Application to Sensitivity Analysis
• Adaptive Ridge Selector (ARiS)
• A Metric-learning based framework for Support Vector Machines and Multiple Kernel Learning
• A Feature Selection Based on Perturbation Theory
• Refitting solutions promoted by ${\ell }_{12}$ sparse analysis regularization with block penalties
• Lasso tuning through the flexible-weighted bootstrap
• Network estimation in State Space Model with L1-regularization constraint
• An Adapted Geographically Weighted Lasso(Ada-GWL) model for estimating metro ridership
• A Global Homogeneity Test for High-Dimensional Linear Regression
• Time delay estimation in satellite imagery time series of precipitation and NDVI: Pearson's cross correlation revisited
• Bridging Information Criteria and Parameter Shrinkage for Model Selection
• Sparse Representation Classification via Screening for Graphs
• Large scale Lasso with windowed active set for convolutional spike sorting
• Inference of genetic networks from time course expression data using functional regression with lasso penalty
• Penalized Variable Selection in Multi-Parameter Regression Survival Modelling
• Efficient Test-based Variable Selection for High-dimensional Linear Models
• Correlation and Class Based Block Formation for Improved Structured Dictionary Learning
• Recovering Non-negative and Combined Sparse Representations
• Multi-dimensional sparse structured signal approximation using split Bregman iterations
• Learning Stable Multilevel Dictionaries for Sparse Representations
• Distributional Consistency of Lasso by Perturbation Bootstrap
• Joint Screening Tests for LASSO
• Probabilistic Available Delivery Capability Assessment of General Distribution Network with Renewables
• gLOP: the global and Local Penalty for Capturing Predictive Heterogeneity
• Sparsity by Worst-Case Penalties
• A New Performance Guarantee for Orthogonal Matching Pursuit Using Mutual Coherence
• Performance Analysis of Parameter Estimation Using LASSO
• Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations
• Multiuser Media-based Modulation for Massive MIMO Systems
• Sparse regularization for fiber ODF reconstruction: from the suboptimality of ${\ell }_2$ and ${\ell }_1$ priors to ${\ell }_0$
• Significance Testing and Group Variable Selection
• Boosting Dictionary Learning with Error Codes
• LMM-Lasso: A Lasso Multi-Marker Mixed Model for Association Mapping with Population Structure Correction
• Parameter Selection Algorithm For Continuous Variables
• Prediction Weighted Maximum Frequency Selection
• Robust and sparse estimation methods for high dimensional linear and logistic regression
• Least Absolute Gradient Selector: Statistical Regression via Pseudo-Hard Thresholding
• Randomized pick-freeze for sparse Sobol indices estimation in high dimension
• Local-Aggregate Modeling for Big-Data via Distributed Optimization: Applications to Neuroimaging
• Path Following in the Exact Penalty Method of Convex Programming
• Alternating Linearization for Structured Regularization Problems
• On best subset regression
• Fast tree inference with weighted fusion penalties
• Beyond Support in Two-Stage Variable Selection
• Smooth blockwise iterative thresholding: a smooth fixed point estimator based on the likelihood's block gradient
• Integration of Gene Expression Data and Methylation Reveals Genetic Networks for Glioblastoma
• High-dimensional regression with unknown variance
• An \ell_1-oracle inequality for the Lasso in finite mixture of multivariate Gaussian regression models
• A Greedy Homotopy Method for Regression with Nonconvex Constraints
• Autoregressive Process Modeling via the Lasso Procedure
• Matrix Coherency Graph: A Tool for Improving Sparse Coding Performance
• Convex Techniques for Model Selection
• An Algorithm for Quadratic ${\ell }_1$-Regularized Optimization with a Flexible Active-Set Strategy
• Sparse recovery with unknown variance: a LASSO-type approach
• Nonparametric Model Checking and Variable Selection
• Global Sensitivity Analysis of High Dimensional Neuroscience Models: An Example of Neurovascular Coupling
• A robust l_1 penalized DOA estimator
• Scaling Multiple-Source Entity Resolution using Statistically Efficient Transfer Learning
• Epi-convergent Smoothing with Applications to Convex Composite Functions
• Proximal methods for the latent group lasso penalty
• Stochastic Stepwise Ensembles for Variable Selection
• A network of spiking neurons for computing sparse representations in an energy efficient way
• An efficient algorithm for structured sparse quantile regression
• An Iterative Algorithm for Fitting Nonconvex Penalized Generalized Linear Models with Grouped Predictors
• Anti-Sampling-Distortion Compressive Wideband Spectrum Sensing for Cognitive Radio
• Practical error estimates for sparse recovery in linear inverse problems
• Learning sparse gradients for variable selection and dimension reduction
• A projection proximal-point algorithm for l^1-minimization
• Evaluating the diagnostic powers of variables and their linear combinations when the gold standard is continuous
• Recovering Direct Effects in Genetics: A Comparison
• Target Detection via Network Filtering
• Nonparametric Bayesian Classification
• Weak convergence of the regularization path in penalized M-estimation
• Compressed Sensing with Cross Validation
• Cross Validation for Penalized Quantile Regression with a Case-Weight Adjusted Solution Path
• Perturbative estimation of stochastic gradients
• Cost-sensitive Selection of Variables by Ensemble of Model Sequences
• Topological Techniques in Model Selection
• MSP: A Multi-step Screening Procedure for Sparse Recovery