arXiv:2405.13535v5 Announce Type: replace-cross Abstract: In recent years, inconsistency in Bayesian deep learning has attracted significant attention. Tempered or generalized posterior distributions are frequently employed as direct and effective solutions. Nonetheless, the underlying mechanisms and the effectiveness of generalized posteriors…
arXiv:2411.04551v2 Announce Type: replace-cross Abstract: Transformers are deep neural network architectures that underpin the recent successes of large language models. Unlike more classical architectures that can be viewed as point-to-point maps, a Transformer acts as a measure-to-measure map implemented as…
arXiv:2509.18037v1 Announce Type: new Abstract: We consider the problem of clustering a sample of probability distributions from a random distribution on $mathbb R^p$. Our proposed partitioning method makes use of a symmetric, positive-definite kernel $k$ and its associated reproducing kernel…
arXiv:2502.14790v5 Announce Type: replace-cross Abstract: We develop a form Thompson sampling for online learning under full feedback – also known as prediction with expert advice – where the learner’s prior is defined over the space of an adversary’s future actions,…
arXiv:2509.18047v1 Announce Type: new Abstract: In this paper, we present a general specification for Functional Effects Models, which use Machine Learning (ML) methodologies to learn individual-specific preference parameters from socio-demographic characteristics, therefore accounting for inter-individual heterogeneity in panel choice data.…
arXiv:2509.16233v1 Announce Type: cross Abstract: We present a probabilistic framework to accurately estimate dimensions of additively manufactured components. Using a dataset of 405 parts from nine production runs involving two machines, three polymer materials, and two-part configurations, we examine five…
arXiv:2509.16337v1 Announce Type: cross Abstract: Multi-centre studies increasingly rely on distributed inference, where sites share only centre-level summaries. Homogeneity of parameters across centres is often violated, motivating methods that both emph{test} for equality and emph{learn} centre groupings before estimation. We…
arXiv:2509.16411v1 Announce Type: cross Abstract: Dual encoder (DE) models, where a pair of matching query and document are embedded into similar vector representations, are widely used in information retrieval due to their simplicity and scalability. However, the Euclidean geometry of…
arXiv:2501.00755v2 Announce Type: replace Abstract: Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, treatment, and outcome variables. The core innovation of…
arXiv:2509.16451v1 Announce Type: cross Abstract: Adaptive robust optimization (ARO) extends static robust optimization by allowing decisions to depend on the realized uncertainty – weakly dominating static solutions within the modeled uncertainty set. However, ARO makes previous constraints that were independent…
