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arXiv:2603.03662v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful framework for processing graph-structured data. However, conventional GNNs and their variants …
arXiv:2603.03672v1 Announce Type: new Abstract: The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition …
arXiv:2603.03673v1 Announce Type: new Abstract: Stein's identity is a fundamental tool in machine learning with applications in generative models, stochastic optimization, and other problems involving …
arXiv:2603.03725v1 Announce Type: new Abstract: The volume of freely scraped data on the Internet has driven the tremendous success of deep learning. Along with this …
arXiv:2603.03748v1 Announce Type: new Abstract: High-stakes synthetic data generation faces a fundamental Quadrilemma: achieving Fidelity to the original distribution, Control over complex logical constraints, Reliability …
arXiv:2603.03756v1 Announce Type: new Abstract: While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, leaving the …
arXiv:2603.03760v1 Announce Type: new Abstract: Time Series forecasting (TSF) in the modern era faces significant computational and storage cost challenges due to the massive scale …
arXiv:2603.03777v1 Announce Type: new Abstract: A typical Vertical Federated Learning (VFL) scenario involves several participants collaboratively training a machine learning model, where each party has …
arXiv:2603.03778v1 Announce Type: new Abstract: We study the Inverse Contextual Bandit (ICB) problem, in which a learner seeks to optimize a policy while an observer, …
arXiv:2603.03796v1 Announce Type: new Abstract: When continual test-time adaptation (TTA) persists over the long term, errors accumulate in the model and further cause it to …
arXiv:2603.03805v1 Announce Type: new Abstract: Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or …
arXiv:2603.03818v1 Announce Type: new Abstract: Continual learning is a long-standing challenge in robot policy learning, where a policy must acquire new skills over time without …