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arXiv:2603.12530v1 Announce Type: new Abstract: Recent work shows that when contexts are drawn i.i.d., linear contextual bandits can be reduced to single-context linear bandits. This …
arXiv:2603.12540v1 Announce Type: new Abstract: Embedded quantum machine learning (EQML) seeks to bring quantum machine learning (QML) capabilities to resource-constrained edge platforms such as IoT …
arXiv:2603.12541v1 Announce Type: new Abstract: Large language models are often viewed as high-dimensional nonlinear systems and treated as black boxes. Here, we show that transformer …
arXiv:2603.12543v1 Announce Type: new Abstract: Distributed reinforcement learning policies face network delays, jitter, and packet loss when deployed across edge devices and cloud servers. Standard …
arXiv:2603.12544v1 Announce Type: new Abstract: We propose the Deep Distance Measurement Method (DDMM) to improve retrieval accuracy in unsupervised multivariate time series similarity retrieval. DDMM …
arXiv:2603.12552v1 Announce Type: new Abstract: The InfoNCE loss in contrastive learning depends critically on a temperature parameter, yet its dynamics under fixed versus annealed schedules …
arXiv:2603.12556v1 Announce Type: new Abstract: We establish empirical scaling laws for Single-Layer Physics-Informed Neural Networks on canonical nonlinear PDEs. We identify a dual optimization failure: …
arXiv:2603.12557v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are highly vulnerable to adversarial perturbations in both topology and features, making the learning of robust …
arXiv:2603.12576v1 Announce Type: new Abstract: Distributional reinforcement learning (DRL) studies the evolution of full return distributions under Bellman updates rather than focusing on expected values. …
arXiv:2603.12591v1 Announce Type: new Abstract: Federated learning on heterogeneous edge devices requires personalized compression while preserving aggregation compatibility and stable convergence. We present Curvature-Aware Heterogeneous …
arXiv:2603.12594v1 Announce Type: new Abstract: Contrastive learning has achieved remarkable success in self-supervised representation learning, often guided by information-theoretic objectives such as mutual information maximization. …
arXiv:2603.12595v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is a widely used approach to align large-scale AI systems with human values. However, …