arXiv:2503.04684v2 Announce Type: replace Abstract: Filtering-based probabilistic numerical solvers for ordinary differential equations (ODEs), also known as ODE filters, have been established as efficient methods for quantifying numerical uncertainty in the solution of ODEs. In practical applications, however, the underlying…
arXiv:2510.00386v1 Announce Type: cross Abstract: State-of-the-art machine learning often follows a two-stage process: $(i)$~pre-training on large, general-purpose datasets; $(ii)$~fine-tuning on task-specific data. In fine-tuning, selecting training examples that closely reflect the target distribution is crucial. However, it is often the…
arXiv:2510.01168v1 Announce Type: cross Abstract: We study a class of constrained nonconvex–nonconcave minimax problems in which the inner maximization involves potentially complex constraints. Under the assumption that the inner problem of a novel lifted minimax problem satisfies a local Kurdyka-{L}ojasiewicz…
arXiv:2510.00309v1 Announce Type: cross Abstract: The Lipschitz bandit problem extends stochastic bandits to a continuous action set defined over a metric space, where the expected reward function satisfies a Lipschitz condition. In this work, we introduce a new problem of…
arXiv:2510.01022v1 Announce Type: cross Abstract: We introduce a novel version of the geometric scattering transform for geometric graphs containing scalar and vector node features. This new scattering transform has desirable symmetries with respect to rigid-body roto-translations (i.e., $SE(3)$-equivariance) and may…
arXiv:2509.04008v2 Announce Type: replace-cross Abstract: Stein variational gradient descent (SVGD) is a kernel-based and non-parametric particle method for sampling from a target distribution, such as in Bayesian inference and other machine learning tasks. Different from other particle methods, SVGD does…
arXiv:2506.24007v3 Announce Type: replace-cross Abstract: This study investigates minimax and Bayes optimal strategies in fixed-budget best-arm identification. We consider an adaptive procedure consisting of a sampling phase followed by a recommendation phase, and we design an adaptive experiment within this…
arXiv:2510.00079v1 Announce Type: cross Abstract: We introduce textbf{Directed Information $gamma$-covering}, a simple but general framework for redundancy-aware context engineering. Directed information (DI), a causal analogue of mutual information, measures asymmetric predictiveness between chunks. If $operatorname{DI}_{i to j} ge H(C_j) –…
arXiv:2505.15626v2 Announce Type: replace-cross Abstract: Concept-based explanation methods aim at making machine learning models more transparent by finding the most important semantic features of an input (e.g., colors, patterns, shapes) for a given prediction task. However, these methods generally ignore…
arXiv:2510.00048v1 Announce Type: cross Abstract: Early and accurate diagnosis of Alzheimer Disease is critical for effective clinical intervention, particularly in distinguishing it from Mild Cognitive Impairment, a prodromal stage marked by subtle structural changes. In this study, we propose a…
