arXiv:2604.00904v2 Announce Type: replace Abstract: Learning to defer (L2D) enables human-AI cooperation by deciding when an AI system should act autonomously or defer to a human expert. Existing L2D methods, however, assume static human performance, contradicting well-established findings on fatigue-induced…
arXiv:2604.03599v1 Announce Type: new Abstract: For a larger set of predictions of several differently trained machine learning models, known as bagging predictors, the mean of all predictions is taken by default. Nevertheless, this proceeding can deviate from the actual ground…
arXiv:2502.06096v5 Announce Type: replace-cross Abstract: This paper addresses a fundamental but largely unexplored challenge in sequential changepoint analysis: conducting inference following a detected change. We develop a very general framework to construct confidence sets for the unknown changepoint using only…
arXiv:2604.03606v1 Announce Type: new Abstract: Federated learning (FL) research increasingly relies on single-node simulations with hundreds or thousands of virtual clients, making both efficiency and reproducibility essential. Yet parallel client training often introduces nondeterminism through shared random state and scheduling…
arXiv:2508.10208v2 Announce Type: replace-cross Abstract: Traditional models for pricing catastrophe (CAT) bonds struggle to capture the complex, relational data inherent in these instruments. This paper introduces CATNet, a novel framework that applies a geometric deep learning architecture, the Relational Graph…
Researchers developed a system that intelligently balances workloads to improve the efficiency of flash storage hardware in a data center.
arXiv:2604.04872v1 Announce Type: cross Abstract: As large language model agents advance beyond software engineering (SWE) tasks toward machine learning engineering (MLE), verifying agent behavior becomes orders of magnitude more expensive: while SWE tasks can be verified via fast-executing unit tests,…
arXiv:2512.18503v3 Announce Type: replace-cross Abstract: Synthetic Aperture Radar (SAR) offers a unique capability for all-weather, space-based maritime activity monitoring by capturing and imaging strong reflections from ships at sea. A well-defined challenge in this domain is ship type classification. Due…
arXiv:2603.29496v2 Announce Type: replace-cross Abstract: We present Metriplector, a neural architecture primitive in which the input configures an abstract physical system — fields, sources, and operators — and the dynamics of that system is the computation. Multiple fields evolve via…
arXiv:2603.28921v2 Announce Type: replace Abstract: Standard neural network training uses constant momentum (typically 0.9), a convention dating to 1964 with limited theoretical justification for its optimality. We derive a time-varying momentum schedule from the critically damped harmonic oscillator: mu(t) =…
