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arXiv:2603.23582v1 Announce Type: new Abstract: Accurate sleep staging is essential for diagnosing OSA and hypopnea in stroke patients. Although PSG is reliable, it is costly, …
arXiv:2603.23584v1 Announce Type: new Abstract: Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading …
arXiv:2603.23626v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as optimization modules in agentic systems, yet the fundamental limits of such LLM-mediated …
arXiv:2603.23629v1 Announce Type: new Abstract: Code LLMs often default to particular programming languages and libraries under neutral prompts. We investigate whether these preferences are encoded …
arXiv:2603.23658v1 Announce Type: new Abstract: Gradient boosting, a method of building additive ensembles from weak learners, has established itself as a practical and theoretically-motivated approach …
arXiv:2603.23719v1 Announce Type: new Abstract: Electronic health records (EHRs) are invaluable for clinical research, yet privacy concerns severely restrict data sharing. Synthetic data generation offers …
arXiv:2603.23738v1 Announce Type: new Abstract: A major challenge of Reinforcement Learning is that agents often learn undesired behaviors that seem to defy the reward structure …
arXiv:2603.23746v1 Announce Type: new Abstract: Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges. …
arXiv:2603.23755v1 Announce Type: new Abstract: Curriculum learning improves reinforcement learning (RL) efficiency by sequencing tasks from simple to complex. However, many self-paced curriculum methods rely …
arXiv:2603.23780v1 Announce Type: new Abstract: Large Language Models (LLMs) have introduced new capabilities to recommender systems, enabling dynamic, context-aware, and conversational recommendations. However, LLM-based recommender …
arXiv:2603.23783v1 Announce Type: new Abstract: Adapting large-scale foundation models to new domains with limited supervision remains a fundamental challenge due to latent distribution mismatch, unstable …
arXiv:2603.23784v1 Announce Type: new Abstract: Grokking-the phenomenon where validation accuracy of neural networks on modular addition of two integers rises long after training data has …
