arXiv research

Paper proves conditions for estimating precision matrices with Laplacian constraints.

Proposes a neural network method to improve consistencies in high dimensional data analysis.

Lasso proves consistent model selection for high-dimensional Ising models.

Paper proposes a new sparsity scheme for high-dimensional VAR models.

We show that the two-stage adaptive Lasso procedure (Zou, 2006) is consistent for high-dimensional model selection in linear and Gaussian graphical models. Our conditions for consistency cover more general situations than those accomplished in previous work: we prove that restricted eigenvalue conditions (Bickel et al.…

High-dimensional regression models struggle with resampling methods.

We show that minimum-norm interpolation in the Reproducing Kernel Hilbert Space corresponding to the Laplace kernel is not consistent if input dimension is constant. The lower bound holds for any choice of kernel bandwidth, even if selected based on data. The result supports the empirical observation that minimum-norm …

Study reveals how high-dimensional models are vulnerable to consistent adversarial attacks.

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

The paper optimizes hyperplanes for binary classification in high-dimensional data with latent Gaussian mixtures.

Model selection is crucial to high-dimensional learning and inference for contemporary big data applications in pinpointing the best set of covariates among a sequence of candidate interpretable models. Most existing work assumes implicitly that the models are correctly specified or have fixed dimensionality. Yet both …

We consider the problem of extracting a low-dimensional, linear latent variable structure from high-dimensional random variables. Specifically, we show that under mild conditions and when this structure manifests itself as a linear space that spans the conditional means, it is possible to consistently recover the struc…

Develops inequalities for high-dimensional linear processes with dependent innovations.

Regularized M-estimators are used in diverse areas of science and engineering to fit high-dimensional models with some low-dimensional structure. Usually the low-dimensional structure is encoded by the presence of the (unknown) parameters in some low-dimensional model subspace. In such settings, it is desirable for est…

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

In this work, we develop a novel principal component analysis (PCA) for semimartingales by introducing a suitable spectral analysis for the quadratic variation operator. Motivated by high-dimensional complex systems typically found in interest rate markets, we investigate correlation in high-dimensional high-frequency …

The paper introduces a method for interpretable principal component analysis of high-dimensional time series.

A VB method for high-dimensional regression with student-t priors achieves nearly optimal performance and computational efficiency.

Trans-Ising combines auxiliary datasets to estimate high-dimensional Ising models.

New theorem for generalized group sparsity improves consistency and convergence rates.

There is an increasing body of evidence suggesting that exact nearest neighbour search in high-dimensional spaces is affected by the curse of dimensionality at a fundamental level. Does it necessarily mean that the same is true for k nearest neighbours based learning algorithms such as the k-NN classifier? We analyse t…

Estimates CATEs using high-dimensional linear regression models.

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

Paper introduces semi-supervised linear extremile regression for high-dimensional data.

A new method improves graph-based learning for high-dimensional data.

Proposes FarmHazard model for hazard regression with correlated covariates.

Develops high-dimensional measurement error models for non-linear loss functions.

The paper develops methods to estimate the high-dimensional efficient frontier without distributional assumptions.

Analysis of cross-validation for early-stopped gradient descent in high-dimensional regression.

The study introduces a high-dimensional tail index model for viral post analysis.

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

HD-BWDM improves clustering validation in high-dimensional data.

We consider principal component analysis for contaminated data-set in the high dimensional regime, where the dimensionality of each observation is comparable or even more than the number of observations. We propose a deterministic high-dimensional robust PCA algorithm which inherits all theoretical properties of its ra…

New method filters large networks from financial data to reveal key subnetworks.

We consider selection of random predictors for high-dimensional regression problem with binary response for a general loss function. Important special case is when the binary model is semiparametric and the response function is misspecified under parametric model fit. Selection for such a scenario aims at recovering th…

Online SGD achieves consistent estimation in high-dimensional non-convex inference tasks.

High-dimensional kernel regression struggles due to rotational invariance.

Efficient clustering in high dimensions with Quick Shift and LSH.

Estimating causal effects for survival outcomes in the high-dimensional setting is an extremely important topic for many biomedical applications as well as areas of social sciences. We propose a new orthogonal score method for treatment effect estimation and inference that results in asymptotically valid confidence int…

This study evaluates clustering algorithms on high-dimensional data.

IBPF algorithm tackles high-dimensional parameter learning for complex systems.

Paper addresses regret minimization and inference in high-dimensional online decision-making.

Generalizes causal inference to high-dimensional outcomes.

Paper analyzes adaptive Lasso for high-dimensional diffusion processes, improving support recovery and bias.

Investigates the impact of finite VC dimension on neural network approximation and learning.

We consider the high-dimensional discriminant analysis problem. For this problem, different methods have been proposed and justified by establishing exact convergence rates for the classification risk, as well as the l2 convergence results to the discriminative rule. However, sharp theoretical analysis for the variable…

GCAO improves clustering of high-dimensional data by grouping low-density boundary points.

We consider constraint-based methods for causal structure learning, such as the PC-, FCI-, RFCI- and CCD- algorithms (Spirtes et al. (2000, 1993), Richardson (1996), Colombo et al. (2012), Claassen et al. (2013)). The first step of all these algorithms consists of the PC-algorithm. This algorithm is known to be order-d…