arXiv research

E&E uses contrastive learning to speed up SBI for high-dimensional systems.

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

Two-parameter models can learn high-dimensional targets via gradient flow.

Paper efficiently infers differential parameters in time-varying models using time score matching.

Paper tackles high-dimensional quantile regression with distribution shift using transfer learning.

Enhances VAR model estimation using transfer learning.

SGE-Kriging reduces high-dimensional surrogate modelling costs.

Paper characterizes optimal learning trajectories for high-dimensional nonlinear models.

The paper provides guarantees for high-dimensional DML estimators in observational studies.

Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these …

As machine learning systems get widely adopted for high-stake decisions, quantifying uncertainty over predictions becomes crucial. While modern neural networks are making remarkable gains in terms of predictive accuracy, characterizing uncertainty over the parameters of these models is challenging because of the high d…

We present novel, computationally efficient, and differentially private algorithms for two fundamental high-dimensional learning problems: learning a multivariate Gaussian and learning a product distribution over the Boolean hypercube in total variation distance. The sample complexity of our algorithms nearly matches t…

Biological data including gene expression data are generally high-dimensional and require efficient, generalizable, and scalable machine-learning methods to discover their complex nonlinear patterns. The recent advances in machine learning can be attributed to deep neural networks (DNNs), which excel in various tasks i…

A new method reduces high-dimensional data's impact on CWMs using TSNE.

Neural Galerkin schemes use active learning to solve high-dimensional equations.

Penalized (or regularized) regression, as represented by Lasso and its variants, has become a standard technique for analyzing high-dimensional data when the number of variables substantially exceeds the sample size. The performance of penalized regression relies crucially on the choice of the tuning parameter, which d…

Deep Penalty Method solves high-dimensional optimal stopping problems using deep learning.

Proposes a new prior for complex models to improve prediction accuracy.

Machine learning improves high-dimensional matrix estimation.

Develops Bayesian filtering for online learning and related problems.

A new method for solving complex inverse problems using deep learning.

A new method optimizes slicing directions for SW distances to improve high-dimensional probability measure comparison.

We consider learning high-dimensional multi-response linear models with structured parameters. By exploiting the noise correlations among responses, we propose an alternating estimation (AltEst) procedure to estimate the model parameters based on the generalized Dantzig selector. Under suitable sample size and resampli…

A privacy-preserving algorithm for high-dimensional bandits.

Optimal and safe semi-supervised learning estimator for high-dimensional data.

We provide a way to infer about existence of topological circularity in high-dimensional data sets in $\mathbb{R}^d$ from its projection in $\mathbb{R}^2$ obtained through a fast manifold learning map as a function of the high-dimensional dataset $\mathbb{X}$ and a particular choice of a positive real $σ$ known as band…

Improved online PCA algorithm learns from evolving norm of parameter vector.

We propose a likelihood ratio based inferential framework for high dimensional semiparametric generalized linear models. This framework addresses a variety of challenging problems in high dimensional data analysis, including incomplete data, selection bias, and heterogeneous multitask learning. Our work has three main …

Proposes a model for classifying high-dimensional time series with interpretable parameters.

Topological data analysis and its main method, persistent homology, provide a toolkit for computing topological information of high-dimensional and noisy data sets. Kernels for one-parameter persistent homology have been established to connect persistent homology with machine learning techniques. We contribute a kernel…

Direct contextual policy search methods learn to improve policy parameters and simultaneously generalize these parameters to different context or task variables. However, learning from high-dimensional context variables, such as camera images, is still a prominent problem in many real-world tasks. A naive application o…

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…

Paper explores differential privacy in high-dimensional federated learning, tackling server trustworthiness and estimation.

The paper explores high-dimensional learning in finance, proving key aspects and setting lower bounds.

Linear mixed models (LMMs) are used extensively to model dependecies of observations in linear regression and are used extensively in many application areas. Parameter estimation for LMMs can be computationally prohibitive on big data. State-of-the-art learning algorithms require computational complexity which depends …

We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models. In particular, we make two contributions: (i) For parameter estimation, we propose a novel high dimensional EM algorithm which naturally incorporates sparsity structure into parameter estima…

This paper improves system identification by reducing sample complexity for high-dimensional linear dynamical systems.

Deep learning model solves high-dimensional PDEs using Actor-Critic approach.

We consider the problem of multi-task learning in the high dimensional setting. In particular, we introduce an estimator and investigate its statistical and computational properties for the problem of multiple connected linear regressions known as Data Enrichment/Sharing. The between-tasks connections are captured by a…

A scalable method for accurate inference of low-dimensional parameters in high-dimensional linear regression.

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

We consider estimating a low-dimensional parameter in an estimating equation involving high-dimensional nuisances that depend on the parameter. A central example is the efficient estimating equation for the (local) quantile treatment effect ((L)QTE) in causal inference, which involves as a nuisance the covariate-condit…

A model learns causal representations from high-dimensional data.

Learning rate needs to decrease with higher data moments for effective ICA in high dimensions.

Tensor Neural Networks solve high-dimensional PDEs for financial pricing.

We present a new method for high-dimensional linear regression when a scale parameter of the additive errors is unknown. The proposed estimator is based on a penalized Huber $M$-estimator, for which theoretical results on estimation error have recently been proposed in high-dimensional statistics literature. However, t…

The paper analyzes bootstrap ensemble classifiers in high-dimensional settings.

The present contribution suggests the use of a multidimensional scaling (MDS) algorithm as a visualization tool for manifold-valued elements. A visualization tool of this kind is useful in signal processing and machine learning whenever learning/adaptation algorithms insist on high-dimensional parameter manifolds.