arXiv:2501.02436v5 Announce Type: replace-cross Abstract: Advancements in artificial intelligence call for a deeper understanding of the fundamental mechanisms underlying deep learning. In this work, we propose…
arXiv:2607.06936v1 Announce Type: cross Abstract: This paper studies transfer learning for linear discriminant analysis in high-dimensional two-class classification. We consider one target domain and several source…
arXiv:2601.13102v3 Announce Type: replace Abstract: Full conformal prediction is a framework that implicitly formulates distribution-free confidence prediction regions for a wide range of estimators. However, a…
arXiv:2607.06879v1 Announce Type: cross Abstract: Best-arm identification is a canonical model for data-driven decision-making, but in many applications each reward observation is costly. Motivated by the…
arXiv:2509.21160v2 Announce Type: replace Abstract: With the growing use of large language models, concerns over content authenticity have spurred a variety of watermarking schemes. These schemes…
arXiv:2607.06652v1 Announce Type: cross Abstract: Rough path signatures are a universal feature map for continuous paths and, via the expected signature, characterise path distributions. These guarantees…
arXiv:2606.31126v2 Announce Type: replace-cross Abstract: Predicting biomolecular properties from limited labeled data is a central bottleneck in protein engineering and small-molecule design. As strong pretrained encoders…
arXiv:2607.07527v1 Announce Type: new Abstract: Artificial intelligence (AI) is a double-edged sword: while it has achieved remarkable success across a wide range of domains, its deployment…
arXiv:2606.19084v2 Announce Type: replace-cross Abstract: We consider the problem of score function estimation via empirical risk minimization. We first start with the question of inferring the…
arXiv:2607.06696v1 Announce Type: new Abstract: Weighted empirical measures on compact manifolds arise in importance sampling, particle approximations, posterior summaries, quadrature, and representation learning. Standard weight-only summaries,…
arXiv:2607.06841v1 Announce Type: new Abstract: Diffusion models offer a powerful framework for sampling from complex probability densities by learning to reverse a noising process. A common…
arXiv:2607.06883v1 Announce Type: new Abstract: We consider the problem of finding stationary points for stochastic convex optimization problems. Rather than surrogates to stationarity, such as a…
arXiv:2607.07232v1 Announce Type: new Abstract: Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these…
arXiv:2607.06644v1 Announce Type: new Abstract: Determinantal point processes have recently emerged as a kernel-based alternative to standard independent sampling for constructing efficient minibatches, coresets, and other…
arXiv:2607.07468v1 Announce Type: new Abstract: We study the recovery of sparse functions from finite, noisy, and indirect observations in the framework of statistical inverse learning. The…
arXiv:2512.20685v3 Announce Type: replace Abstract: Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from…
arXiv:2505.19033v2 Announce Type: replace Abstract: Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by constructing prediction sets with user-specified marginal coverage guarantees.…
arXiv:2605.23145v2 Announce Type: replace Abstract: Individual fairness, the notion that "similar individuals should be treated similarly," provides a strong and flexible fairness guarantee for algorithmic decision…
arXiv:2603.23196v2 Announce Type: replace-cross Abstract: In this work, we investigate Gaussian Mixture Models ({it abbrv} GMM) and the related problem of non parametric maximum likelihood estimation…
arXiv:2601.20498v2 Announce Type: replace-cross Abstract: Diffusion models provide a principled framework for generative modeling via stochastic differential equations and time-reversed dynamics. However, extension of spectral diffusion…
arXiv:2607.06731v1 Announce Type: new Abstract: The Beagle framework is a GPU-based genetic programming framework that enables highly efficient genetic programming search using large population sizes by…
arXiv:2607.07373v1 Announce Type: new Abstract: In biological circuits, sequential neural activity evolves along dynamic, low-dimensional manifolds to enable flexible behavior. Spiking network models link aspects of…
arXiv:2607.07429v1 Announce Type: new Abstract: In this work, we reviewed different approaches in mathematical modeling of biologically plausible neural systems. Models are characterized and classified based…
arXiv:2607.07460v1 Announce Type: new Abstract: Navigation for social organisms rarely is a fully independent activity. Group structure and dynamics, as well as embodied interactions, critically influence…