arXiv:2509.15143v1 Announce Type: new Abstract: This paper proposes the Next-Depth Lookahead Tree (NDLT), a single-tree model designed to improve performance by evaluating node splits not only at the node being optimized but also by evaluating the quality of the next…
arXiv:2509.15198v1 Announce Type: cross Abstract: Deep learning has significantly advanced electrocardiogram (ECG) analysis, enabling automatic annotation, disease screening, and prognosis beyond traditional clinical capabilities. However, understanding these models remains a challenge, limiting interpretation and gaining knowledge from these developments. In…
arXiv:2509.14502v1 Announce Type: cross Abstract: The weighted average treatment effect (WATE) defines a versatile class of causal estimands for populations characterized by propensity score weights, including the average treatment effect (ATE), treatment effect on the treated (ATT), on controls (ATC),…
arXiv:2509.14522v1 Announce Type: cross Abstract: We study the open-set label shift problem, where the test data may include a novel class absent from training. This setting is challenging because both the class proportions and the distribution of the novel class…
arXiv:2412.03486v3 Announce Type: replace Abstract: Contrastive representation learning is a modern paradigm for learning representations of unlabeled data via augmentations — precisely, contrastive models learn to embed semantically similar pairs of samples (positive pairs) closer than independently drawn samples (negative…
arXiv:2509.14827v1 Announce Type: cross Abstract: Cortical surface reconstruction (CSR) from magnetic resonance imaging (MRI) is fundamental to neuroimage analysis, enabling morphological studies of the cerebral cortex and functional brain mapping. Recent advances in learning-based CSR have dramatically accelerated processing, allowing…
arXiv:2509.14969v1 Announce Type: cross Abstract: We introduce a new adaptive step-size strategy for convex optimization with stochastic gradient that exploits the local geometry of the objective function only by means of a first-order stochastic oracle and without any hyper-parameter tuning.…
arXiv:2411.11697v2 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has proven effective in solving complex decision-making tasks across various domains, but challenges remain in continuous-time settings, particularly when state dynamics are governed by stochastic differential equations (SDEs) with jump components. In…
arXiv:2505.10876v2 Announce Type: replace-cross Abstract: We address the problem of detecting anomalies as samples that do not conform to structured patterns represented by low-dimensional manifolds. To this end, we conceive a general anomaly detection framework called Preference Isolation Forest (PIF),…
arXiv:2509.15060v1 Announce Type: cross Abstract: We present a smooth probabilistic reformulation of $ell_0$ regularized regression that does not require Monte Carlo sampling and allows for the computation of exact gradients, facilitating rapid convergence to local optima of the best subset…
