arXiv Statistics ML
Functional BART with Shape Priors: A Bayesian Tree Approach to Constrained Functional Regression
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Flow-Transformed Implicit Processes for Function-Space Variational Inference
arXiv:2606. 01954v1 Announce Type: new Abstract: Implicit-process priors define distributions over functions through flexible generative mechanisms, making them attractive for Bayesian function-space modelling.
arXiv Machine Learning
Aug 14
Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks
arXiv:2605. 07060v3 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) provide a mesh-free framework for solving PDE-constrained inverse problems, but their extension to Bayesian inversion still faces a fundamental difficulty: prior distributions are typically defined in the weight space of neural networks, whereas physically meaningful prior assumptions are more naturally expressed in function space.
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Structured Nonparametric Variational Inference for Dependent Latent Modeling
arXiv:2606. 15458v1 Announce Type: cross Abstract: Variational inference (VI) is a core engine of modern AI, enabling scalable approximate Bayesian learning and uncertainty-aware training of large probabilistic and generative models.
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Jun 4
Sparse Bayesian Deep Functional Learning with Structured Region Selection
arXiv:2602. 20651v3 Announce Type: replace Abstract: In modern applications such as ECG monitoring, neuroimaging, wearable sensing, and industrial equipment diagnostics, complex and continuously structured data are ubiquitous, presenting both challenges and opportunities for functional data analysis.
arXiv Statistics ML
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Seeing the Forest for the Trees: The Gaussian Process Limit of BART
arXiv:2607.28844v2 Announce Type: replace-cross Abstract: Bayesian Additive Regression Trees (BART) have shown state-of-the-art performance in both prediction and causal inference problems. Previous...
arXiv Machine Learning
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Deep Shape Regression for Planar Curves with Multimodal Covariates
arXiv:2607. 19600v1 Announce Type: cross Abstract: The shape of a planar curve is the geometric information that remains once translation, rotation, scale and reparametrisation are removed and is of interest in many health applications, e.