research
∙
09/05/2022
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference
Aided by advances in neural density estimation, considerable progress ha...
share
research
∙
07/08/2022
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning experiments and open-source training ...
research
∙
06/15/2022
Calibrating Agent-based Models to Microdata with Graph Neural Networks
Calibrating agent-based models (ABMs) to data is among the most fundamen...
research
∙
02/23/2022
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Simulation models of complex dynamics in the natural and social sciences...
research
∙
06/23/2021
Learning Multimodal VAEs through Mutual Supervision
Multimodal VAEs seek to model the joint distribution over heterogeneous ...
research
∙
06/23/2021
Approximate Bayesian Computation with Path Signatures
Simulation models of scientific interest often lack a tractable likeliho...
research
∙
04/13/2021
Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics
High-dimensional limit theorems have been shown to be useful to derive t...
research
∙
11/17/2020
Generalized Posteriors in Approximate Bayesian Computation
Complex simulators have become a ubiquitous tool in many scientific disc...
research
∙
06/17/2020
Rethinking Semi-Supervised Learning in VAEs
We present an alternative approach to semi-supervision in variational au...
research
∙
06/08/2020
Neural ODEs for Multi-State Survival Analysis
Survival models are a popular tool for the analysis of time to event dat...
research
∙
12/02/2019
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks
Bayesian interpretations of neural network have a long history, dating b...
research
∙
03/03/2019
Bernoulli Race Particle Filters
When the weights in a particle filter are not available analytically, st...
research
∙
06/26/2018