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OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection

arXiv:2507.21164v2 Announce Type: replace Abstract: Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where anomalous samples are rare or not available. Most state-of-the-art methods fall into two categories: reconstruction-based approaches,…

Mechanical Field Networks: Structured Neural Dynamics for Multivariate Systems

arXiv:2606.11251v1 Announce Type: new Abstract: Many multivariate dynamical systems are observed only through trajectories, leaving the mechanisms governing their joint dynamics hidden. Existing approaches can impose interpretable dynamics or learn flexible state transitions, yet the resulting interaction structure is typically…

On Regret Bounds of Thompson Sampling for Bayesian Optimization

arXiv:2603.09276v2 Announce Type: replace-cross Abstract: We study a widely used Bayesian optimization method, Gaussian process Thompson sampling (GP-TS), under the assumption that the objective function is a sample path from a GP. Compared with the GP upper confidence bound (GP-UCB)…

Corpus Augmentation for Sign Language Translation via LLM-Guided Video Stitching

arXiv:2606.11925v1 Announce Type: cross Abstract: Sign language translation (SLT) converts sign language video into spoken language text and holds significant promise for improving accessibility and enabling communication between signing and non-signing communities. While large weakly-aligned datasets have enabled pre-training at…

Few-Shot Resampling for Scalable Statistically-Sound Data Mining

arXiv:2606.11235v1 Announce Type: new Abstract: A key step in knowledge discovery is the evaluation of data mining results. In several applications, including pattern mining, graph analysis, and others, this step includes the evaluation of the statistical significance of the results,…

Bernstein-Schur Kernels: Random Features by Sketched Modulation and Radial Randomization

arXiv:2606.11255v2 Announce Type: new Abstract: Bernstein–Schur kernels are products of a finite-feature kernel and a completely monotone shift-invariant kernel: nonstationary kernels falling between the shift-invariant and dot-product templates random features exploit, so neither Bochner sampling nor polynomial sketching applies to…