arXiv:2402.08992v2 Announce Type: replace-cross Abstract: High-probability guarantees in stochastic optimization are often obtained only under strong noise assumptions such as sub-Gaussian tails. We show that such guarantees can also be achieved under the weaker assumption of bounded variance by developing…
arXiv:2510.03131v1 Announce Type: cross Abstract: Modern regression analyses are often undermined by covariate measurement error, misspecification of the regression model, and misspecification of the measurement error distribution. We present, to the best of our knowledge, the first Bayesian nonparametric framework…
arXiv:2412.05788v2 Announce Type: replace-cross Abstract: With the advent of diffusion models, new proteins can be generated at an unprecedented rate. The motif scaffolding problem requires steering this generative process to yield proteins with a desirable functional substructure called a motif.…
arXiv:2510.03164v1 Announce Type: cross Abstract: Learning rate warm-up – increasing the learning rate at the beginning of training – has become a ubiquitous heuristic in modern deep learning, yet its theoretical foundations remain poorly understood. In this work, we provide…
arXiv:2501.07186v3 Announce Type: replace-cross Abstract: Factors such as the proliferation of renewable energy and electrification contribute to grid congestion as a pressing problem. Topology control is an appealing method for relieving congestion, but traditional approaches for topology discovery have proven…
arXiv:2510.03199v1 Announce Type: cross Abstract: LLM inference often generates a batch of candidates for a prompt and selects one via strategies like majority voting or Best-of- N (BoN). For difficult tasks, this single-shot selection often underperforms. Consequently, evaluations commonly report…
arXiv:2505.05168v2 Announce Type: replace-cross Abstract: The present paper solves the problem of local linear approximation of the Fr’echet conditional mean in an extrinsic and intrinsic way from time correlated bivariate curve data evaluated in a manifold (see Torres et al,…
arXiv:2510.03226v1 Announce Type: cross Abstract: Finite mixtures are a cornerstone of Bayesian modelling, and it is well-known that sampling from the resulting posterior distribution can be a hard task. In particular, popular reversible Markov chain Monte Carlo schemes are often…
arXiv:2505.13189v2 Announce Type: replace-cross Abstract: We adopt a Gamma and Malliavin Calculi point of view in order to generalize Score-based diffusion Generative Models (SGMs) to an infinite-dimensional abstract Hilbertian setting. Particularly, we define the forward noising process using Dirichlet forms…
arXiv:2207.00108v2 Announce Type: replace Abstract: Machine learning algorithms are routinely used for business decisions that may directly affect individuals, for example, because a credit scoring algorithm refuses them a loan. It is then relevant from an ethical (and legal) point…
