arXiv Statistics ML
Functional BART with Shape Priors: A Bayesian Tree Approach to Constrained Functional Regression
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arXiv Machine Learning
Jun 2
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.
arXiv Machine Learning
Oct 8
RoBART: Bayesian Additive Regression Trees with Tree-Specific Rotations
arXiv:2610.10214v1 Announce Type: cross Abstract: Bayesian additive regression trees (BART) can require many splits to approximate boundaries misaligned with the predictor axes. RoBART assigns each t...
arXiv Machine Learning
Jun 16
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.
arXiv Machine Learning
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
Oct 28
Two-sample comparison through additive tree models for density ratios
The paper introduces additive tree models for estimating the density ratio between two distributions, using a novel balancing loss that enables training with supervised learning algorithms such as forward‑stagewise optimization and gradient boosting. The loss also functions as a pseudo‑likelihood, allowing generalized Bayesian inference via backfitting samplers for Bayesian additive regression trees, which provides uncertainty quantification. Experiments show that the boosting algorithms achieve high accuracy with low computational cost, and the Bayesian approach offers competitive accuracy while delivering uncertainty estimates, demonstrated on microbiome compositional data.