arXiv research

A new particle filter avoids resampling to improve state estimation in high dimensions.

Improved state estimation in high-dimensional models using Zig-Zag Sampler.

A new method reduces high-dimensional state space for dynamic choice models.

Algorithm estimates human decision-making in high-dimensional states with finite-time guarantees.

ETGPSSM efficiently models high-dimensional, non-stationary systems with reduced complexity.

Algorithm learns diffusion processes with high-dimensional state spaces.

AD-EnKFs use machine learning to improve data assimilation in high-dimensional systems.

In recent years, deep reinforcement learning has been shown to be adept at solving sequential decision processes with high-dimensional state spaces such as in the Atari games. Many reinforcement learning problems, however, involve high-dimensional discrete action spaces as well as high-dimensional state spaces. This pa…

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

SSNL improves simulation-based inference for high-dimensional data.

New AMP algorithm detects change points in high-dimensional GLMs.

The paper analyzes neural networks for solving high-dimensional Schrödinger eigenvalue problems.

A new method for analyzing high-dimensional time-series data using deep neural networks.

Approximate message passing (AMP) refers to a class of efficient algorithms for statistical estimation in high-dimensional problems such as compressed sensing and low-rank matrix estimation. This paper analyzes the performance of AMP in the regime where the problem dimension is large but finite. For concreteness, we co…

Novel approach integrates Multivariate Square-root Lasso into Synthetic Control for high-dimensional data.

We study the performance of Local Causal Discovery (LCD), a simple and efficient constraint-based method for causal discovery, in predicting causal effects in large-scale gene expression data. We construct practical estimators specific to the high-dimensional regime. Inspired by the ICP algorithm, we use an optional pr…

Deep learning (DL) is a high dimensional data reduction technique for constructing high-dimensional predictors in input-output models. DL is a form of machine learning that uses hierarchical layers of latent features. In this article, we review the state-of-the-art of deep learning from a modeling and algorithmic persp…

Efficiently simulates slow dynamics of high-dimensional stochastic systems.

Bayesian Neural Networks improve high-dimensional level set estimation.

Paper analyzes high-dimensional GMM for SSL, showing RMLE near-optimal performance.

We simplify Bayesian filtering by framing it as optimization, making it practical for high-dimensional systems.

A new real-time method estimates system matrices and states using Kalman filter.

A neural network method estimates entropy production from system trajectories.

TRE improves density-ratio estimation for highly dissimilar densities.

We focus on the problem of estimating the change in the dependency structures of two $p$-dimensional Gaussian Graphical models (GGMs). Previous studies for sparse change estimation in GGMs involve expensive and difficult non-smooth optimization. We propose a novel method, DIFFEE for estimating DIFFerential networks via…

The use of M-estimators in generalized linear regression models in high dimensional settings requires risk minimization with hard $L_0$ constraints. Of the known methods, the class of projected gradient descent (also known as iterative hard thresholding (IHT)) methods is known to offer the fastest and most scalable sol…

GATSBI uses GANs for SBI, improving posterior estimation in high dimensions.

A framework for forecasting high-dimensional time-series data using clustering.

Mixtures-of-Experts (MoE) are conditional mixture models that have shown their performance in modeling heterogeneity in data in many statistical learning approaches for prediction, including regression and classification, as well as for clustering. Their estimation in high-dimensional problems is still however challeng…

Improves DRL for long-term causal inference with semiparametric methods.

An active learning algorithm for the classification of high-dimensional images is proposed in which spatially-regularized nonlinear diffusion geometry is used to characterize cluster cores. The proposed method samples from estimated cluster cores in order to generate a small but potent set of training labels which prop…

New framework for discrete-state diffusion models reduces sample complexity.

Nested model averaging improves high-dimensional linear regression performance.

OptIMIS method improves SRAM yield estimation efficiency and accuracy.

RCUKF combines data-driven modeling and Bayesian estimation for accurate system state estimation.

EnSF improves accuracy in tracking high-dimensional nonlinear systems.

Randomized value functions offer a promising approach towards the challenge of efficient exploration in complex environments with high dimensional state and action spaces. Unlike traditional point estimate methods, randomized value functions maintain a posterior distribution over action-space values. This prevents the …

We present a high-dimensional analysis of three popular algorithms, namely, Oja's method, GROUSE and PETRELS, for subspace estimation from streaming and highly incomplete observations. We show that, with proper time scaling, the time-varying principal angles between the true subspace and its estimates given by the algo…

SRMC framework reduces Monte Carlo variance by history-based sampling in high-dimensional spaces.

Framework expands particle filtering to estimate states beyond prior boundaries.

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 …

t-NEB clusters high-dimensional data hierarchically with density paths.

Paper proposes new density estimators for high-dimensional data.

Proposes an INLA-based method for state and parameter estimation in nonlinear systems.

EGL optimizes complex functions without fitting them, achieving state-of-the-art results.

Frank-Wolfe algorithm (FW) and its variants have gained a surge of interests in machine learning community due to its projection-free property. Recently people have reduced the gradient evaluation complexity of FW algorithm to $\log(\frac{1}ε)$ for the smooth and strongly convex objective. This complexity result is esp…

Federated framework learns causal states to predict counterfactuals without centralizing data.

Machine learning improves high-dimensional matrix estimation.