3 citations · 9 across the 26 of their papers we have counts for
5 papers · 1 filter
Prediction-Centric Uncertainty Quantification via MMD
Zheyang Shen, Jeremias Knoblauch, Sam Power +1
Deterministic mathematical models, such as those specified via differential equations, are a powerful tool to communicate scientific insight. However, such models are necessarily s…
Grand Challenges in Bayesian Computation
Anirban Bhattacharya, Antonio Linero, Chris. J. Oates
This article appeared in the September 2024 issue (Vol. 31, No. 3) of the Bulletin of the International Society for Bayesian Analysis (ISBA).
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…
Reinforcement Learning for Adaptive MCMC
Congye Wang, Wilson Chen, Heishiro Kanagawa +1
An informal observation, made by several authors, is that the adaptive design of a Markov transition kernel has the flavour of a reinforcement learning task. Yet, to-date it has re…
Probabilistic Richardson Extrapolation
Chris. J. Oates, Toni Karvonen, Aretha L. Teckentrup +2
For over a century, extrapolation methods have provided a powerful tool to improve the convergence order of a numerical method. However, these tools are not well-suited to modern c…