Accurate solar power forecasting plays a critical role in ensuring the reliable and economic
operation of power grids. Most of existing literature directly uses available weather …

Graph learning from data is a canonical problem that has received substantial attention in
the literature. Learning a structured graph is essential for interpretability and identification of …

Index tracking is a popular passive portfolio management strategy that aims at constructing a
portfolio that replicates or tracks the performance of a financial index. The tracking error can …

Recent years have witnessed a surge of interest in parallel and distributed optimization
methods for large-scale systems. In particular, nonconvex large-scale optimization problems …

Learning a graph with a specific structure is essential for interpretability and identification of
the relationships among data. But structured graph learning from observed samples is an …

Statistical inference for principal components of spiked covariance matrices Page 1 The Annals
of Statistics 2022, Vol. 50, No. 2, 1144–1169 https://doi.org/10.1214/21-AOS2143 © Institute of …

Load fluctuations, unexpected disturbances, and switching of operating states typically make
actual industrial processes exhibit nonstationary. In nonstationary processes, the statistical …

This paper proposes a framework for optimizing cost functions of orthonormal basis learning
problems, such as principal component analysis (PCA), subspace recovery, orthogonal …

Noise, redundancy, and dynamic characteristics in industrial process data have been
regarded as the key factors that affect the measurement accuracy of data-driven soft …

This paper defines fair principal component analysis (PCA) as minimizing the maximum
mean discrepancy (MMD) between the dimensionality-reduced conditional distributions of …