Every story across every category, newest first. Each card links to the original publisher; daily-brief posts open as editorial pages.
arXiv:2605.30253v3 Announce Type: replace Abstract: We study the non-asymptotic contraction in Wasserstein distance of the sequential, parallel, and random-scan coordinate ascent variational inference algorithms. This is…
arXiv:2607.00252v1 Announce Type: cross Abstract: We present an algorithm for the group distributionally robust (GDR) least squares problem. Given $m$ groups, a parameter vector in $mathbb{R}^d$,…
arXiv:2607.01171v1 Announce Type: cross Abstract: Sample-based generative models are increasingly used for probabilistic forecasting in high-stakes decision settings, yet their training objectives are blind to the…
arXiv:2607.00224v1 Announce Type: cross Abstract: Watermarking promises a statistical trace of large language model (LLM) use, but real documents, after editing or paraphrasing, rarely arrive as…
arXiv:2607.00645v1 Announce Type: cross Abstract: Multi-task regression aims at jointly solving multiple regression problems, called tasks. Compared to solving each task separately, better performances can be…
arXiv:2607.00207v1 Announce Type: cross Abstract: We develop a framework for analyzing the learning dynamics of $ell_2$-adversarial training of single-index models on Gaussian mixtures in the high-dimensional…
arXiv:2607.00470v1 Announce Type: new Abstract: We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time. The parameters of the…
arXiv:2607.00877v1 Announce Type: new Abstract: Traditional variational Kalman filtering with unknown noise statistics suffers from inconsistent process covariance estimation and slow convergence speed, limiting its practical…
arXiv:2607.00995v1 Announce Type: new Abstract: Most existing multitask learning approaches are limited by their reliance on task-specific loss functions tailored to the scale and type of…
arXiv:2607.01010v1 Announce Type: new Abstract: The success of deep learning models in classification and regression is widely attributed to the low-dimensional structure that real-world data tend…
arXiv:2606.27525v2 Announce Type: cross Abstract: The 2024 Department of Justice antitrust complaint against RealPage, Inc. named five major residential REITs for coordinating algorithmic rent pricing across…
arXiv:2607.01057v1 Announce Type: new Abstract: We study a broad class of graphical models whose independencies correspond to vertex separation in mixed graphs with directed, undirected, and…
arXiv:2607.00149v1 Announce Type: cross Abstract: We study uniform-in-time propagation-of-chaos for continuous-time Stein Variational Gradient Descent (SVGD). Classical finite-time propagation-of-chaos estimates for mean-field systems typically deteriorate rapidly…
arXiv:2510.06995v2 Announce Type: replace Abstract: We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several…
arXiv:2607.00669v1 Announce Type: cross Abstract: Autoencoders (AEs) have emerged as powerful tools for non-linear dimensionality reduction, often surpassing traditional linear methods such as Proper Orthogonal Decomposition…
arXiv:2504.15388v3 Announce Type: replace-cross Abstract: In the context of multivariate nonparametric regression with missing covariates, we propose Pattern Embedded Neural Networks (PENNs), which can be applied…
arXiv:2606.20299v2 Announce Type: replace Abstract: Deep learning has managed to evade numerous intuitions from classical statistics to achieve unprecedented performance on a number of real-world tasks.…
arXiv:2606.10111v2 Announce Type: replace-cross Abstract: This paper presents a nonlinear parameter estimator for Wiener-type state-space models obtained as a fixed-point architecture that couples two affine minimum…
arXiv:2603.08001v3 Announce Type: replace-cross Abstract: Maximum inner product search (MIPS) is a crucial subroutine in machine learning, requiring the identification of a vector taken within a…
arXiv:2606.21639v2 Announce Type: replace-cross Abstract: Minimum Spanning Trees have been used in unsupervised learning, particularly in clustering tasks, due to their ability to recognize clusters by…
arXiv:2607.00286v1 Announce Type: new Abstract: Oscillatory neural networks (ONNs) have emerged as a promising neuromorphic architecture, leveraging coupled dynamical systems to perform computation and represent information…
arXiv:2607.00761v1 Announce Type: new Abstract: Evolutionary multitasking (EMT) has shown strong capability in solving multiple optimization problems simultaneously by exploiting latent inter-task consistency, such as similarities…
arXiv:2607.00846v1 Announce Type: new Abstract: This paper studies whether the Metabolic Multi-Agent Optimizer (MMAO) can be credibly derived into a dynamic-optimization method without replacing its core…
arXiv:2604.21468v2 Announce Type: replace Abstract: Local Optima Networks (LONs) represent the global structure of search spaces as graphs, but their construction requires iterative execution of a…