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Found 5 papers, 2 papers with code
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
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 $n$.
Towards a Testable Notion of Generalization for Generative Adversarial Networks
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.
On Nesting Monte Carlo Estimators
Many problems in machine learning and statistics involve nested expectations and thus do not permit conventional Monte Carlo (MC) estimation.
Online Learning Rate Adaptation with Hypergradient Descent
We introduce a general method for improving the convergence rate of gradient-based optimizers that is easy to implement and works well in practice.
On the Pitfalls of Nested Monte Carlo
In this paper, we analyse the behaviour of nested Monte Carlo (NMC) schemes, for which classical convergence proofs are insufficient.
