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arXiv:2603.15871v1 Announce Type: new Abstract: Following the pivotal success of learning strategies to win at tasks, solely by interacting with an environment without any supervision, …
arXiv:2603.15880v1 Announce Type: new Abstract: Electrodermal Activity (EDA) is a non-invasive physiological signal widely available in wearable devices and reflects sympathetic nervous system (SNS) activation. …
arXiv:2603.15886v1 Announce Type: new Abstract: We present PhasorFlow, an open-source Python library introducing a computational paradigm operating on the $S^1$ unit circle. Inputs are encoded …
arXiv:2603.15901v1 Announce Type: new Abstract: This dissertation investigates privacy-preserving federated learning for Alzheimer's disease classification using three-dimensional MRI data from the Alzheimer's Disease Neuroimaging Initiative …
arXiv:2603.15907v1 Announce Type: new Abstract: Game theory provides the gold standard for analyzing adversarial engagements, offering strong optimality guarantees. However, these guarantees often become brittle …
arXiv:2603.15914v1 Announce Type: new Abstract: AI tools and agents are reshaping how researchers work, from proving theorems to training neural networks. Yet for many, it …
arXiv:2603.15916v1 Announce Type: new Abstract: When LLM agents autonomously design ML experiments, do they perform genuine architecture search -- or do they default to hyperparameter …
arXiv:2603.15925v1 Announce Type: new Abstract: Inverse design aims to find design parameters $x$ achieving target performance $y^*$. Generative approaches learn bidirectional mappings between designs and …
arXiv:2603.15926v1 Announce Type: new Abstract: Causal discovery in health data faces evaluation challenges when ground truth is unknown. We address this by collaborating with experts …
arXiv:2603.15927v1 Announce Type: new Abstract: We propose a data-driven framework to learn interaction kernels in stochastic multi-agent systems. Our approach aims at identifying the functional …
arXiv:2603.15939v1 Announce Type: new Abstract: Applying machine learning to sensitive time-series data is often bottlenecked by the iteration loop: Performance depends strongly on preprocessing and …
arXiv:2603.15957v1 Announce Type: new Abstract: Asymmetric self-play has emerged as a promising paradigm for post-training large language models, where a teacher continually generates questions for …