1 code implementation • NeurIPS 2023 • Tobias Schröder, Zijing Ou, Jen Ning Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew B. Duncan
Energy-based models are a simple yet powerful class of probabilistic models, but their widespread adoption has been limited by the computational burden of training them.
no code implementations • 31 Dec 2020 • Anthony D. Blaom, Sebastian J. Vollmer
A graph-based protocol called `learning networks' which combine assorted machine learning models into meta-models is described.
1 code implementation • 23 Jul 2020 • Anthony D. Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer
MLJ (Machine Learing in Julia) is an open source software package providing a common interface for interacting with machine learning models written in Julia and other languages.
4 code implementations • 16 Jan 2017 • Joris Bierkens, Alexandre Bouchard-Côté, Arnaud Doucet, Andrew B. Duncan, Paul Fearnhead, Thibaut Lienart, Gareth Roberts, Sebastian J. Vollmer
Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration.
Methodology Computation
no code implementations • 21 Nov 2016 • Jackson Gorham, Andrew B. Duncan, Sebastian J. Vollmer, Lester Mackey
Stein's method for measuring convergence to a continuous target distribution relies on an operator characterizing the target and Stein factor bounds on the solutions of an associated differential equation.
no code implementations • 14 Sep 2016 • Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer
Based on this, we develop relativistic stochastic gradient descent by taking the zero-temperature limit of relativistic stochastic gradient Hamiltonian Monte Carlo.
3 code implementations • 8 Oct 2015 • Alexandre Bouchard-Côté, Sebastian J. Vollmer, Arnaud Doucet
We explore and propose several original extensions of an alternative approach introduced recently in Peters and de With (2012) where the target distribution of interest is explored using a continuous-time Markov process.
Methodology Statistics Theory Statistics Theory
no code implementations • 2 Jan 2015 • Sebastian J. Vollmer, Konstantinos C. Zygalakis, and Yee Whye Teh
For this toy model we study the gain of the SGLD over the standard Euler method in the limit of large data sets.