digitado

arXiv:2606.06855v1 Announce Type: new Abstract: While algorithmic stability is a central tool for understanding generalization of learning algorithms, existing high-probability guarantees typically rely on uniform boundedness or sub-Gaussian/sub-Weibull tail assumptions, which can be overly restrictive for modern settings with heavy-tailed or unbounded losses. We develop a stability-based framework that requires only a finite $L_p$ moment condition. Our first contribution is sharp concentration inequalities for functions of independent random variables under $L_p$ constraints, extending McDiarmid’s bounded-differences techniques beyond the […]

Ver mais

arXiv:2606.06814v1 Announce Type: new Abstract: The transformer’s emergent ability to perform in-context learning (ICL) has sparked a wide range of studies designed to understand its underlying mechanisms. Existing works often study how training task diversity, defined either as the number of ICL training task vectors or as the number of function classes from which the task vectors are drawn, shapes both the learning dynamics and generalization capabilities of ICL. While both definitions have uncovered many interesting phenomena, many […]

Ver mais

arXiv:2606.06785v1 Announce Type: new Abstract: We study finite-sample change detection for one-dimensional noisy dynamical systems using partition-based empirical approximations of stationary behaviour. Given observations from an interval-valued process, we partition the state space, estimate a finite transition matrix from observed transitions between partition elements, and apply a small Doeblin-type regularisation to ensure a unique stationary distribution. From an initial reference segment, we compute a baseline empirical stationary distribution (widehat{pi}_{0,rho}). For each later sliding window, we compute (widehat{pi}_{t,rho}) and […]

Ver mais

arXiv:2606.06772v1 Announce Type: new Abstract: Understanding the generalization performance of over-parameterized neural networks has become a central topic in deep learning theory. While recent advances, particularly works under the Neural Tangent Kernel (NTK) regime, have shed light on the behavior of shallow architectures, the statistical generalization properties of deep neural networks (DNNs), especially in regression tasks, remain far less understood. In this paper, we make significant progress toward closing this gap by providing a comprehensive generalization analysis of […]

Ver mais

arXiv:2606.06764v1 Announce Type: new Abstract: Recent progress has been made in understanding the statistical generalization performance of gradient descent methods for overparameterized neural networks within the neural tangent kernel (NTK) regime. However, most of the existing work on regression problems is limited to shallow network architectures, leaving a notable gap in the theory of deep neural networks. This paper addresses this gap by presenting a comprehensive generalization analysis for deep ReLU networks trained using gradient descent (GD) and […]

Ver mais

arXiv:2606.06502v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly trained on scraped web corpora without authorisation, content owners require forensic methods to prove that their documents were included in a model’s training set. We propose textbf{SIGIL} (textbf{S}ubtle textbf{I}njection for textbf{G}round-truth textbf{I}nference of textbf{L}LM training data), a framework that embeds imperceptible emph{canary sequences} into protected text and code such that any LLM trained on those documents exhibits statistically detectable behavioural signatures when probed with targeted queries. […]

Ver mais

arXiv:2606.06501v1 Announce Type: new Abstract: The advancement of malware poses obstacles for cybersecurity, necessitating the development of advanced detection techniques. This paper proposes an approach to enhance malware detection through the use of a generative artificial intelligence model. Specifically, variational autoencoders (VAEs) are used with the random forest, XGBoost and sequential model machine learning classifiers. Generated synthetic malware samples are used to address the critical issue of data scarcity for new or less common malware types. This approach […]

Ver mais

arXiv:2606.06500v1 Announce Type: new Abstract: Lattice structures are of growing importance in additive manufacturing, where complex internal geometries are increasingly required for lightweight, high surface-to-volume ratios, multifunctionality, and other superior mechanical properties. Conventional lattice modeling methods typically represent struts with simple primitives, such as cylinders or cones, limiting geometric diversity and the design space. Although recent efforts have increased strut-shape complexity to address this issue, they often do so at the expense of computational efficiency and modeling robustness. […]

Ver mais