arXiv Machine Learning
Geometry-Aware Block Selection for Sparse Variational Gaussian Processes
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arXiv Machine Learning
Jun 30
Warm-Starting Iterative Gaussian Processes for Faster Sequential Inference
arXiv:2511. 16340v2 Announce Type: replace Abstract: Efficient Gaussian process (GP) inference is critical for sequential decision-making tasks such as active learning, online prediction, and Bayesian optimization.
arXiv Machine Learning
Oct 9
Feature Space Adaptation for Effortless Gaussian Process Flows
The paper introduces improvements to FlowGP, a method for conditional sampling from Gaussian processes outside the linear-Gaussian regime. It adds kernel approximations to scale to high-resolution domains and a new technique for computing marginal likelihood by measuring the work required to steer diffusion toward conditioning statements. These advances enable hyperparameter optimisation within FlowGP and are demonstrated on tasks such as probabilistic downscaling, PDE solution inference on irregular domains, and sea level anomaly field recovery from non-Gaussian satellite data.
arXiv Machine Learning
Jun 3
Scalable Derivative Gaussian Processes via Exact Gradient Reduction
arXiv:2606. 02909v1 Announce Type: cross Abstract: Gradient observations can substantially improve Gaussian process (GP) surrogates, particularly in high-dimensional settings where function evaluations are expensive.
arXiv Machine Learning
Sep 21
Triply-Scalable Equivariant Gaussian Process Modeling
arXiv:2609. 21085v1 Announce Type: cross Abstract: Gaussian processes (GPs) provide principled probabilistic predictions while encoding prior knowledge, including equivariances.
arXiv AI
3d ago
Learning to Be Guided: Shaping Latent Geometry for Classifier-Free Guidance
arXiv:2610.22885v1 Announce Type: cross Abstract: Classifier-free guidance (CFG) is a standard component of conditional diffusion models, and guided generation performance has become the \textit{de f...
arXiv Machine Learning
Sep 14
A Generalized Tangent Approximation based Variational Inference Framework for Strongly Super-Gaussian Likelihoods
The paper introduces a new variational inference framework that uses tangent transformations to handle strongly super‑Gaussian likelihoods across a wide range of probability models. By constructing tangent minorants of the log‑likelihood through convex duality, the method achieves conjugacy with Gaussian priors, enabling tractable inference where traditional approaches struggle. The authors provide algorithmic convergence guarantees and near‑parametric risk bounds, and demonstrate superior scalability and accuracy on both simulated and real‑world datasets compared to existing variational algorithms.