1 code implementation • 28 Jan 2019 • Robert Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet
Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods is too computationally intensive to handle large datasets, since the cost per step usually scales like $\Theta(n)$ in the number of data points $n$.
no code implementations • ICLR 2018 • Robert Cornish, Hongseok Yang, Frank Wood
We consider the question of how to assess generative adversarial networks, in particular with respect to whether or not they generalise beyond memorising the training data.
no code implementations • ICML 2018 • Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington, Frank Wood
Many problems in machine learning and statistics involve nested expectations and thus do not permit conventional Monte Carlo (MC) estimation.
3 code implementations • ICLR 2018 • Atilim Gunes Baydin, Robert Cornish, David Martinez Rubio, Mark Schmidt, Frank Wood
We introduce a general method for improving the convergence rate of gradient-based optimizers that is easy to implement and works well in practice.
no code implementations • 3 Dec 2016 • Tom Rainforth, Robert Cornish, Hongseok Yang, Frank Wood
In this paper, we analyse the behaviour of nested Monte Carlo (NMC) schemes, for which classical convergence proofs are insufficient.