arXiv research

BO method identifies sparse subspaces for efficient high-dimensional optimization.

Develops methods for estimating and providing confidence bands in sparse high-dimensional additive models.

Improved Thompson Sampling for high-dimensional sparse bandits.

Sparse VAE learns latent factors from high-dimensional data.

We introduce a new algorithm, called adaptive sparse backfitting algorithm, for solving high dimensional Sparse Additive Model (SpAM) utilizing symmetric, non-negative definite smoothers. Unlike the previous sparse backfitting algorithm, our method is essentially a block coordinate descent algorithm that guarantees to …

Improved Sparse Polyak for high-dimensional M-estimation with sparser solutions.

A privacy-preserving algorithm for high-dimensional bandits.

PROBE algorithm efficiently solves sparse high-dimensional linear regression.

The paper improves Bayesian precision matrix estimation for high-dimensional sparse data.

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

Improved VB algorithm for high-dimensional logistic regression with theoretical guarantees.

Fitting high-dimensional data involves a delicate tradeoff between faithful representation and the use of sparse models. Too often, sparsity assumptions on the fitted model are too restrictive to provide a faithful representation of the observed data. In this paper, we present a novel framework incorporating sparsity i…

Sparse graph learning for dependent time series using ADMM.

Many conventional statistical procedures are extremely sensitive to seemingly minor deviations from modeling assumptions. This problem is exacerbated in modern high-dimensional settings, where the problem dimension can grow with and possibly exceed the sample size. We consider the problem of robust estimation of sparse…

Sparse APCA identifies sparse factors in financial returns over time.

We propose a novel sparse tensor decomposition method, namely Tensor Truncated Power (TTP) method, that incorporates variable selection into the estimation of decomposition components. The sparsity is achieved via an efficient truncation step embedded in the tensor power iteration. Our method applies to a broad family …

In this article the package High-dimensional Metrics (\texttt{hdm}) is introduced. It is a collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dim…

SP-SPCA improves sparse PCA by adaptively adjusting variable penalties, enhancing interpretability and stability.

The paper analyzes sparse high-dimensional linear regression with random design and unknown error variance, providing adaptiveness and concentration rates.

We consider high-dimensional binary classification by sparse logistic regression. We propose a model/feature selection procedure based on penalized maximum likelihood with a complexity penalty on the model size and derive the non-asymptotic bounds for the resulting misclassification excess risk. The bounds can be reduc…

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

A new distributed algorithm for fitting sparse additive models with feature division and decorrelation.

New method improves robust sparse association estimation.

A new method scales sparse machine learning to ultra-high dimensional problems.

In this paper, we address the challenging problem of selecting tuning parameters for high-dimensional sparse regression. We propose a simple and computationally efficient method, called path thresholding (PaTh), that transforms any tuning parameter-dependent sparse regression algorithm into an asymptotically tuning-fre…

Paper proposes sparse classification method for high-dimensional data.

We establish minimax optimal rates of convergence for estimation in a high dimensional additive model assuming that it is approximately sparse. Our results reveal an interesting phase transition behavior universal to this class of high dimensional problems. In the {\it sparse regime} when the components are sufficientl…

SDAMI enhances interpretable high-dimensional regression with sparse deep learning and footprint principle.

New method infers graph from dependent matrix data.

SOLAR improves search efficiency and accuracy with sparse, orthogonal embeddings.

Many models for sparse regression typically assume that the covariates are known completely, and without noise. Particularly in high-dimensional applications, this is often not the case. This paper develops efficient OMP-like algorithms to deal with precisely this setting. Our algorithms are as efficient as OMP, and im…

The package High-dimensional Metrics (\Rpackage{hdm}) is an evolving collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dimensional subcomponents…

New GIC improves model selection for structured sparse models.

Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems. Sparsity in GGM plays a central role both statistically and computationally. Unfortunately, real-world data often does not fit well to sparse graphical models. I…

We study parameter estimation in Nonlinear Factor Analysis (NFA) where the generative model is parameterized by a deep neural network. Recent work has focused on learning such models using inference (or recognition) networks; we identify a crucial problem when modeling large, sparse, high-dimensional datasets -- underf…

Canonical Correlation Analysis (CCA) is a classical tool for finding correlations among the components of two random vectors. In recent years, CCA has been widely applied to the analysis of genomic data, where it is common for researchers to perform multiple assays on a single set of patient samples. Recent work has pr…

Forward regression is a statistical model selection and estimation procedure which inductively selects covariates that add predictive power into a working statistical regression model. Once a model is selected, unknown regression parameters are estimated by least squares. This paper analyzes forward regression in high-…

Sparse prototypes improve clustering of high-dimensional directional data.

New method tackles high-dimensional SBL without covariance matrices.

A new R package for high-dimensional regression and precision matrix estimation.

Paper develops a distributed debiased estimator for sparse statistical inference.

New method uses sparse deep neural networks for high-dimensional regression with improved parameter estimation.

In this paper we propose a mixture model, SparseMix, for clustering of sparse high dimensional binary data, which connects model-based with centroid-based clustering. Every group is described by a representative and a probability distribution modeling dispersion from this representative. In contrast to classical mixtur…

Latent-EnSF improves data assimilation for high-dimensional systems with sparse observations.

New algorithm optimizes AUC for sparse high-dimensional data in online learning.

ARGEN method improves variable selection and regularization in high-dimensional sparse models.

Enhances VAR model estimation using transfer learning.

Robust testing of sparse signals in corrupted data.