arXiv:2410.11116v4 Announce Type: replace-cross Abstract: In this paper, we establish a novel connection between the metric entropy growth and the embeddability of function spaces into reproducing…
arXiv:2606.24465v1 Announce Type: cross Abstract: We study the problem of estimating the arrival times of vertices in a uniform random recursive tree from its unlabeled structure.…
arXiv:2606.18729v2 Announce Type: replace Abstract: Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning. For time…
arXiv:2606.24427v1 Announce Type: cross Abstract: Nonlinear mixed-effects models are widely used to analyze longitudinal data, but existing open-source software often supports only a limited subset of…
arXiv:2606.24715v1 Announce Type: new Abstract: We consider the problem of model selection between probabilistic models on data sets of time series. Chosen a proper scoring rule,…
arXiv:2606.24418v1 Announce Type: cross Abstract: Data augmentation is a simple and model-agnostic approach for exploiting known invariances in learning problems. Given a group acting on the…
arXiv:2605.19208v2 Announce Type: replace-cross Abstract: Physical activity (PA) plays an important role in maintaining and improving health. Daily steps have been a key PA measure that…
arXiv:2606.23871v1 Announce Type: cross Abstract: Survival analysis is central to clinical decision-making, yet reliable time-to-event models require large, diverse cohorts that are rarely available at a…
arXiv:2606.23944v1 Announce Type: cross Abstract: State--space models provide a flexible framework for analyzing dynamical systems, yet they often rely on Gaussian assumptions that fail to capture…
arXiv:2606.24244v1 Announce Type: cross Abstract: AI-assisted interviews promise to reduce respondent burden in surveys by allowing respondents to describe experiences naturally while an AI system noisily…
arXiv:2606.24168v1 Announce Type: cross Abstract: Automated diabetic retinopathy (DR) grading from colour fundus photographs can achieve strong predictive performance, but clinical interpretation requires more than an…
arXiv:2606.18074v2 Announce Type: replace Abstract: Causal discovery seeks to uncover the causal dependencies among variables. For this purpose, we propose an algorithm called Tensor-based Second-order Causal…
arXiv:2606.24348v1 Announce Type: cross Abstract: The growing reliance on machine learning for decisions across sectors underscores the importance of model transparency and interpretability. Existing post hoc…
arXiv:2401.14483v4 Announce Type: replace-cross Abstract: In the current practices of machine learning, the evaluation of forecasts has become a cornerstone of scientific progress. A multitude of…
arXiv:2602.16568v2 Announce Type: replace-cross Abstract: Sparse recovery is among the most well-studied problems in learning theory and high-dimensional statistics. In this work, we investigate the statistical…
arXiv:2503.17300v5 Announce Type: replace-cross Abstract: We propose a variational tail bound for norms of random vectors and matrices under moment assumptions on their one-dimensional marginals. A…
arXiv:2605.02701v2 Announce Type: replace-cross Abstract: We propose a robust gradient estimator based on per-sample gradient clipping and analyze its properties both theoretically and empirically. We show…
arXiv:2604.20219v2 Announce Type: replace-cross Abstract: Depth is widely viewed as a central contributor to the success of deep neural networks, whereas standard neural network approximation theory…
arXiv:2606.24166v1 Announce Type: new Abstract: Large Language Models remain vulnerable to adversarial prompts that elicit harmful responses, and scaling red-teaming to cover a broad range of…
arXiv:2606.24588v1 Announce Type: new Abstract: Noise in neural computation is typically regarded as a disturbance, but its spatial distribution may also actively regulate which parts of…
arXiv:2511.08065v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) promise highly energy-efficient computing, but their adoption is hindered by a critical scarcity of event-stream data. This…
arXiv:2606.23874v1 Announce Type: cross Abstract: Understanding how the architecture of neural networks shapes the computations they carry is a central challenge in neuroscience and machine learning.…
arXiv:2601.16096v2 Announce Type: replace Abstract: We introduce Neural Particle Automata (NPA), a Lagrangian generalization of Neural Cellular Automata (NCA) from static lattices to dynamic particle systems.…
arXiv:2601.03509v2 Announce Type: replace-cross Abstract: We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding library of…