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arXiv:2602.23816v1 Announce Type: new Abstract: Given a set of trajectories demonstrating the execution of a task safely in a constrained MDP with observable rewards but …
arXiv:2602.23824v1 Announce Type: new Abstract: Longitudinal electronic health record (EHR) data are often left-censored, making diagnosis records incomplete and unreliable for determining disease onset. In …
arXiv:2602.23827v1 Announce Type: new Abstract: In federated learning (FL), multi-step local updates and data heterogeneity usually lead to sharper global minima, which degrades the performance …
arXiv:2602.23852v1 Announce Type: new Abstract: Automatic sleep stage scoring is crucial for the diagnosis and treatment of sleep disorders. Although deep learning models have advanced …
arXiv:2602.23880v1 Announce Type: new Abstract: This paper develops a theory of graph classification under domain shift through a random-graph generative lens, where we consider intra-class …
arXiv:2602.23947v1 Announce Type: new Abstract: Modern deep neural networks remain challenging to interpret due to the opacity of their latent representations, impeding model understanding, debugging, …
arXiv:2602.23968v1 Announce Type: new Abstract: Masked discrete diffusion models (MDMs) are a promising new approach to generative modelling, offering the ability for parallel token generation …
arXiv:2602.23981v1 Announce Type: new Abstract: Real-world data frequently exhibit latent hierarchical structures, which can be naturally represented by hyperbolic geometry. Although recent hyperbolic neural networks …
arXiv:2602.23994v1 Announce Type: new Abstract: Alzheimer's disease is a progressive neurodegenerative disorder in which mild cognitive impairment (MCI) marks a critical transition between aging and …
arXiv:2602.23997v1 Announce Type: new Abstract: The next generation of autonomous agents must not only learn efficiently but also act reliably and adapt their behavior in …
arXiv:2602.24012v1 Announce Type: new Abstract: Contrastive learning has become a cornerstone of modern representation learning, allowing training with massive unlabeled data for both task-specific and …
arXiv:2602.24066v1 Announce Type: new Abstract: Path signatures provide a rich representation of sequential data, with strong theoretical guarantees and good performance in a variety of …