arXiv research

PopArt efficiently solves sparse linear bandits with tighter recovery guarantees.

Study finds exact limits for sparse regression with fewer observations than usual.

IDS improves sparse linear bandits by balancing information and regret.

High dimensional sparse learning has imposed a great computational challenge to large scale data analysis. In this paper, we are interested in a broad class of sparse learning approaches formulated as linear programs parametrized by a {\em regularization factor}, and solve them by the parametric simplex method (PSM). O…

Proposes FARM model combining latent factor and sparse regression.

Sparse linear models improve neural network debuggability.

Improved online Lasso reduces regret in sparse linear contextual bandits.

This paper explores loss landscapes of sparse neural networks, finding unique characteristics compared to dense networks.

Efficiently estimates sparse linear regression with heavy-tailed data and outliers.

New algorithm reduces sample complexity for sparse linear regression.

Paper proposes distributed sparse multicategory discriminant analysis for classification.

Study shows inefficiency of sparse linear regression learning with fewer than Ω(k^2) samples.

We analyze the computational complexity of Quantum Sparse Support Vector Machine, a linear classifier that minimizes the hinge loss and the $L_1$ norm of the feature weights vector and relies on a quantum linear programming solver instead of a classical solver. Sparse SVM leads to sparse models that use only a small fr…

New insights into statistical and computational limits for mixed sparse linear regression.

In machine learning and data mining, linear models have been widely used to model the response as parametric linear functions of the predictors. To relax such stringent assumptions made by parametric linear models, additive models consider the response to be a summation of unknown transformations applied on the predict…

A new estimator learns sparse linear models with context-dependent coefficients.

Efficiently estimates sparse linear regression with heavy-tailed and outlier-contaminated data.

NGSLL combines DNN accuracy with linear model interpretability.

A two-phase algorithm identifies the best arm in sparse linear bandits with fixed budget.

Paper develops a decoder for sparse codes without encoder matrix, achieving optimal recovery.

Study improves robustness and sparsity in linear regression with adversarial outliers and heavy-tailed noise.

Picasso is a new library for sparse learning problems in R and Python.

The generalized linear model (GLM) plays a key role in regression analyses. In high-dimensional data, the sparse GLM has been used but it is not robust against outliers. Recently, the robust methods have been proposed for the specific example of the sparse GLM. Among them, we focus on the robust and sparse linear regre…

OOMP selects features online for sparse linear regression.

Study on recovering supports of multiple sparse vectors from mixed linear measurements.

FLIPHAT addresses joint differential privacy for high-dimensional sparse linear bandits.

New bounds for high-dimensional sparse linear bandits, balancing information and regret.

Sparse generalized additive models (GAMs) are an extension of sparse generalized linear models which allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. …

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the m…

Fast classification for sparse models, even with correlated features.

In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully Bayesian hierarchy for …

We present a signal representation framework called the sparse manifold transform that combines key ideas from sparse coding, manifold learning, and slow feature analysis. It turns non-linear transformations in the primary sensory signal space into linear interpolations in a representational embedding space while maint…

The paper tackles reward-relevance in offline RL with sparse decision dynamics.

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamenta…

Paper improves sparse linear bandits by accounting for noise variance.

Principal components analysis (PCA) is the optimal linear auto-encoder of data, and it is often used to construct features. Enforcing sparsity on the principal components can promote better generalization, while improving the interpretability of the features. We study the problem of constructing optimal sparse linear a…

Optimal sketching bounds for sparse linear regression under various loss functions are established.

New method discovers causal relationships in sparse linear data.

Sparse linear regression, which entails finding a sparse solution to an underdetermined system of linear equations, can formally be expressed as an $l_0$-constrained least-squares problem. The Orthogonal Least-Squares (OLS) algorithm sequentially selects the features (i.e., columns of the coefficient matrix) to greedil…

Graph neural networks improve AMG convergence for sparse systems.

Efficiently finds sparse solutions to max-plus equations for convex regression.

We are interested in the decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time warping (DTW), which offers particularly successful pairwise motion data comparison, and sparse coding (SC), which enables an…

PROBE algorithm efficiently solves sparse high-dimensional linear regression.

Sparse linear regression is hard to solve efficiently, even with k-sparse solutions.

This paper proposes a fast and accurate method for sparse regression in the presence of missing data. The underlying statistical model encapsulates the low-dimensional structure of the incomplete data matrix and the sparsity of the regression coefficients, and the proposed algorithm jointly learns the low-dimensional s…

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

Efficient algorithms recover two sparse models from a mix of linear queries.

Proposes EM for sparse horseshoe estimation.