Christian Weilbach — accepted AI-conference papers

2021

Sequential core-set Monte Carlo

Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell

UAI 2021poster

Sequential Monte Carlo (SMC) is a general-purpose methodology for recursive Bayesian inference, and is widely used in state space modeling and probabilistic programming. Its resample-move variant reduces the variance of posterior estimates by interleaving Markov chain Monte Carlo (MCMC) steps for pa…

Cited by 2 Source ↗PDF ↗Scholar ↗

2020

Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models

Christian Weilbach, Boyan Beronov, Frank Wood, William Harvey

AISTATS 2020poster

We exploit minimally faithful inversion of graphical model structures to specify sparse continuous normalizing flows (CNFs) for amortized inference. We find that the sparsity of this factorization can be exploited to reduce the numbers of parameters in the neural network, adaptive integration steps…

Cited by 25 Source ↗PDF ↗code ↗Scholar ↗