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arXiv:2602.20396v1 Announce Type: new Abstract: Explainable artificial intelligence promises to yield insights into relevant features, thereby enabling humans to examine and scrutinize machine learning models …
arXiv:2602.20399v1 Announce Type: new Abstract: Neural simulators promise efficient surrogates for physics simulation, but scaling them is bottlenecked by the prohibitive cost of generating high-fidelity …
arXiv:2602.20400v1 Announce Type: new Abstract: To steer language models towards truthful outputs on tasks which are beyond human capability, previous work has suggested training models …
arXiv:2602.20403v1 Announce Type: new Abstract: We study distributionally robust online learning, where a risk-averse learner updates decisions sequentially to guard against worst-case distributions drawn from …
arXiv:2602.20404v1 Announce Type: new Abstract: In tabular Markov decision processes (MDPs) with perfect state observability, each trajectory provides active samples from the transition distributions conditioned …
arXiv:2602.20418v1 Announce Type: new Abstract: Graph neural networks (GNNs) have demonstrated superior performance in various applications, such as recommendation systems and financial risk management. However, …
arXiv:2602.20419v1 Announce Type: new Abstract: Machine Learning as a Service (MLaaS) has emerged as a widely adopted paradigm for providing access to deep neural network …
arXiv:2602.20427v1 Announce Type: new Abstract: Efficient operator scheduling is a fundamental challenge in software compilation and hardware synthesis. While recent differentiable approaches have sought to …
arXiv:2602.20442v1 Announce Type: new Abstract: Machine learning holds great promise for advancing the field of medicine, with electronic health records (EHRs) serving as a primary …
arXiv:2602.20457v1 Announce Type: new Abstract: We study online alignment of large language models under misspecified preference feedback, where the observed preference oracle deviates from an …
arXiv:2602.20461v1 Announce Type: new Abstract: Attention learners, neural networks built on the attention mechanism, e.g., transformers, excel at learning the implicit relationships that relate sequences …
arXiv:2602.20463v1 Announce Type: new Abstract: This paper provides a reinterpretation of the Drifting Model~\cite{deng2026generative} through a semigroup-consistent long-short flow-map factorization. We show that a global …
