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arXiv:2602.13634v1 Announce Type: new Abstract: Neighborhood Aggregation Strategy (NAS) is a widely used approach in graph embedding, underpinning both Graph Neural Networks (GNNs) and Weisfeiler-Lehman …
arXiv:2602.13649v1 Announce Type: new Abstract: Time series chain (TSC) is a recently introduced concept that captures the evolving patterns in large scale time series. Informally, …
arXiv:2602.13651v1 Announce Type: new Abstract: In real-world federated learning (FL) systems, client participation is intermittent, heterogeneous, and often correlated with data characteristics or resource constraints. …
arXiv:2602.13659v1 Announce Type: new Abstract: Fine-tuning large pretrained language models (LLMs) is a cornerstone of modern NLP, yet its growing memory demands (driven by backpropagation …
arXiv:2602.13660v1 Announce Type: new Abstract: In safety-critical applications such as medical image segmentation, prediction systems must provide reliability guarantees that extend beyond conventional expected loss …
arXiv:2602.13666v1 Announce Type: new Abstract: In complex clinical decision-making, clinicians must often track a variety of competing metrics defined by aim (ideal) and limit (strict) …
arXiv:2602.13670v1 Announce Type: new Abstract: Class-incremental learning (CIL) with pre-trained models (PTMs) faces a critical trade-off between efficient adaptation and long-term stability. While analytic learning …
arXiv:2602.13684v1 Announce Type: new Abstract: Correlation Clustering (CC) is a fundamental unsupervised learning primitive whose strongest LP-based approximation guarantees require $\Theta(n^3)$ triangle inequality constraints and …
arXiv:2602.13699v1 Announce Type: new Abstract: Large language models (LLMs) often generate plausible yet incorrect answers, posing risks in safety-critical settings such as medicine. Human evaluation …
arXiv:2602.13700v1 Announce Type: new Abstract: We present the first high-probability optimal regret bound for a policy optimization technique applied to the problem of stochastic contextual …
arXiv:2602.13706v1 Announce Type: new Abstract: We introduce \texttt{OPO-CMDP}, the first policy optimization algorithm for stochastic Contextual Markov Decision Process (CMDPs) under general offline function approximation. …
arXiv:2602.13710v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models enable instruction-following embodied control, but their large compute and memory footprints hinder deployment on resource-constrained robots and …
