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arXiv:2602.13807v1 Announce Type: new Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable …
arXiv:2602.13810v1 Announce Type: new Abstract: Learning expressive and efficient policy functions is a promising direction in reinforcement learning (RL). While flow-based policies have recently proven …
arXiv:2602.13813v1 Announce Type: new Abstract: We introduce Pawsterior, a variational flow-matching framework for improved and extended simulation-based inference (SBI). Many SBI problems involve posteriors constrained …
arXiv:2602.13848v1 Announce Type: new Abstract: We propose a sequential test for detecting arbitrary distribution shifts that allows conformal test martingales (CTMs) to work under a …
arXiv:2602.13857v1 Announce Type: new Abstract: Tasks ranging from sleep staging to clinical diagnosis traditionally rely on standard polysomnography (PSG) devices, bedside monitors and wearable devices, …
arXiv:2602.13910v1 Announce Type: new Abstract: Algorithmic stability is a classical framework for analyzing the generalization error of learning algorithms. It predicts that an algorithm has …
arXiv:2602.13921v1 Announce Type: new Abstract: Repository-level bug localization-the task of identifying where code must be modified to fix a bug-is a critical software engineering challenge. …
arXiv:2602.13934v1 Announce Type: new Abstract: Code generation has progressed more reliably than reinforcement learning, largely because code has an information structure that makes it learnable. …
arXiv:2602.13937v1 Announce Type: new Abstract: Automated Machine Learning (AutoML) has revolutionized the development of data-driven solutions; however, traditional frameworks often function as "black boxes", lacking …
This paper assumes that the mass and charge of a particle are independent of its speed relative to an observer. A particle of mass m …
