75 citations · 146 across the 8 of their papers we have counts for
8 papers
Causal Falsification of Digital Twins
Rob Cornish, Muhammad Faaiz Taufiq, Arnaud Doucet +1
Digital twins are simulation-based models designed to predict how a real-world process will evolve in response to interventions. This modelling paradigm holds substantial promise i…
Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness
Sahra Ghalebikesabi, Rob Cornish, Luke J. Kelly +1
We propose a variational autoencoder architecture to model both ignorable and nonignorable missing data using pattern-set mixtures as proposed by Little (1993). Our model explicitl…
Variational Inference with Continuously-Indexed Normalizing Flows
Anthony Caterini, Rob Cornish, Dino Sejdinovic +1
Continuously-indexed flows (CIFs) have recently achieved improvements over baseline normalizing flows on a variety of density estimation tasks. CIFs do not possess a closed-form ma…
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
Rob Cornish, Anthony L. Caterini, George Deligiannidis +1
We show that normalising flows become pathological when used to model targets whose supports have complicated topologies. In this scenario, we prove that a flow must become arbitra…
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
Robert Cornish, Paul Vanetti, Alexandre Bouchard-Côté +2
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 $Θ(n)…
On Nesting Monte Carlo Estimators
Tom Rainforth, Robert Cornish, Hongseok Yang +2
Many problems in machine learning and statistics involve nested expectations and thus do not permit conventional Monte Carlo (MC) estimation. For such problems, one must nest estim…
Online Learning Rate Adaptation with Hypergradient Descent
Atilim Gunes Baydin, Robert Cornish, David Martinez Rubio +2
We introduce a general method for improving the convergence rate of gradient-based optimizers that is easy to implement and works well in practice. We demonstrate the effectiveness…
On the Pitfalls of Nested Monte Carlo
Tom Rainforth, Robert Cornish, Hongseok Yang +1
There is an increasing interest in estimating expectations outside of the classical inference framework, such as for models expressed as probabilistic programs. Many of these conte…