The article introduces a partial information decomposition (PID) for multivariate Gaussian systems using the Blackwell order, which determines when one channel is more informative than another. It shows that Gaussian channels optimally extract redundant and union information, providing a geometric interpretation and an efficient numerical algorithm. The authors derive closed‑form expressions for two‑source cases, compare their Blackwell redundancy to the BROJA measures, and demonstrate scalability to systems with up to a thousand dimensions or sources, including an application to an optimal control problem.
By Artemy Kolchinsky
The paper introduces CUTS-GPR, a method for exact Gaussian Process Regression (GPR) in high-dimensional settings. It achieves near-linear scaling in the number of training points and low-order polynomial scaling in dimensionality by using an additive kernel on an incomplete grid, enabling efficient kernel matrix-vector products. Benchmarks demonstrate scalability to billions of data points and thousands of dimensions, with full GPR runs on up to 4.5 million points in 500 dimensions and practical application to ten high-dimensional potential energy surfaces completed in hours.
By Mads Greisen H{\o}jlund, August Smart Lykke-M{\o}ller, Henry Moss, Ove Christiansen
arXiv:2606. 27298v1 Announce Type: cross Abstract: We study the fundamental problem of learning a high-dimensional Gaussian truncated to an unknown halfspace.
By Haitong Liu, Deepak Narayanan Sridharan, David Steurer, Manuel Wiedmer
The paper studies the numerical solution of the Beurling‑LASSO (BLASSO) for estimating Gaussian mixture models (GMMs) with unknown numbers of components and unknown diagonal covariance matrices. It introduces a Conic Particle Gradient Descent (CPGD) algorithm that incorporates Riemannian gradient descent to respect the Fisher‑Rao geometry of Gaussian distributions. The authors provide theoretical convergence guarantees, including exponential local convergence under a non‑degeneracy condition related to component separation, and demonstrate through numerical experiments that CPGD is more robust to overspecification of components than the EM algorithm.
By Romane Giard, Yohann De Castro, Roland Denis, Cl\'ement Marteau
arXiv:2606. 31390v1 Announce Type: cross Abstract: Low-rank matrix optimization is often carried out via the Burer-Monteiro (BM) formulation, but choosing the factorization rank $r$ is delicate and can substantially slow optimization.
By Yudong Wei, Liang Zhang, Bingcong Li, Niao He
arXiv:2402.03809v2 Announce Type: replace-cross
Abstract: Gaussian processes are a widely embraced technique for regression due to their good prediction accuracy, analytical tractability, and built-i...
By Mickael Binois (ACUMES), Victor Picheny