4 papers · 1 filter
Extrapolating from Regularised Solutions for Solving Ill-Conditioned Linear Systems in Machine Learning
Disha Hegde, Jon Cockayne, Chris. J. Oates
Rapid prototyping of algorithms is a critical step in modern machine learning. Most algorithms exploit linear algebra, creating a need for lightweight numerical routines which -- w…
Stationary MMD Points
Zonghao Chen, Toni Karvonen, Heishiro Kanagawa +2
Approximation of a target probability distribution using a finite set of points is a problem of fundamental importance in numerical integration. Several authors have proposed to se…
Probabilistic Inference and Learning with Stein's Method
Qiang Liu, Lester Mackey, Chris Oates
This monograph provides a rigorous overview of theoretical and methodological aspects of probabilistic inference and learning with Stein's method. Recipes are provided for construc…
Operator-Informed Score Matching for Markov Diffusion Models
Zheyang Shen, Huihui Wang, Marina Riabiz +1
Diffusion models are typically trained using score matching, a learning objective agnostic to the underlying noising process that guides the model. This paper argues that Markov no…