The Completion Of Covariance Kernels

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This paper presents the experimental validation of a linear recursive state estimation (SE) process for hybrid AC/DC microgrids proposed in the authors' previous work. The SE uses a unified and linear measurement model that relies on the use of synchronize ...

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We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches similar to 20 h(-3) Gpc(3). It is a great challenge to prepare high-resolution simulations with a much ...

The Completion Of Covariance Kernels

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Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Transportation-based functional ANOVA and PCA for covariance operators

Stability: a search for structure

Diffusion Optimistic Learning for Min-Max Optimization

Random matrix methods for high-dimensional machine learning models

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Teacher-Mediated and Student-Led Interaction with a Physics Simulation: Effects on the Learning Experience

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Spectral Estimators for High-Dimensional Matrix Inference

Positive Definite Completions and Continuous Graphical Models

Functional data analysis with rough sample paths?

Detecting whether a stochastic process is finitely expressed in a basis

Robust discrete choice models with t-distributed kernel errors

PINION: physics-informed neural network for accelerating radiative transfer simulations for cosmic reionization

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Reproducibility Dataset for CRYOWRF v1.0

Experimental Validation of a Unified and Linear State Estimation Method for Hybrid AC/DC Microgrids

Nonparametric estimation for SDE with sparsely sampled paths: An FDA perspective

Bayes-optimal Learning of Deep Random Networks of Extensive-width

The DESI N-body Simulation Project - II. Suppressing sample variance with fast simulations