71 citations · 604 across the 67 of their papers we have counts for
6 papers · 2 filters
Statistically Robust Neural Network Classification
Benjie Wang, Stefan Webb, Tom Rainforth
Despite their numerous successes, there are many scenarios where adversarial risk metrics do not provide an appropriate measure of robustness. For example, test-time perturbations…
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
Adam Foster, Martin Jankowiak, Matthew O'Meara +2
We introduce a fully stochastic gradient based approach to Bayesian optimal experimental design (BOED). Our approach utilizes variational lower bounds on the expected information g…
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Yuan Zhou, Hongseok Yang, Yee Whye Teh +1
Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich probabilistic models. They further attempt to automate the process of drawing in…
Amortized Monte Carlo Integration
Adam Goliński, Frank Wood, Tom Rainforth
Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectati…
Hijacking Malaria Simulators with Probabilistic Programming
Bradley Gram-Hansen, Christian Schröder de Witt, Tom Rainforth +3
Epidemiology simulations have become a fundamental tool in the fight against the epidemics of various infectious diseases like AIDS and malaria. However, the complicated and stocha…
Variational Bayesian Optimal Experimental Design
Adam Foster, Martin Jankowiak, Eli Bingham +4
Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by th…