25 citations · 31 across the 6 of their papers we have counts for
6 papers
Learning variational autoencoders via MCMC speed measures
Marcel Hirt, Vasileios Kreouzis, Petros Dellaportas
Variational autoencoders (VAEs) are popular likelihood-based generative models which can be efficiently trained by maximizing an Evidence Lower Bound (ELBO). There has been much pr…
A Bayesian framework for genome-wide inference of DNA methylation levels
Marcel Hirt, Axel Finke, Alexandros Beskos +4
DNA methylation is an important epigenetic mark that has been studied extensively for its regulatory role in biological processes and diseases. WGBS allows for genome-wide measurem…
Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data
Giovanni Ballarin, Petros Dellaportas, Lyudmila Grigoryeva +3
Macroeconomic forecasting has recently started embracing techniques that can deal with large-scale datasets and series with unequal release periods. MIxed-DAta Sampling (MIDAS) and…
Entropy-based adaptive Hamiltonian Monte Carlo
Marcel Hirt, Michalis K. Titsias, Petros Dellaportas
Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCMC) algorithm to sample from an unnormalized probability distribution. A leapfrog integrator is commonly used…
Copula-like Variational Inference
Marcel Hirt, Petros Dellaportas, Alain Durmus
This paper considers a new family of variational distributions motivated by Sklar's theorem. This family is based on new copula-like densities on the hypercube with non-uniform mar…
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers
Marcel Hirt, Petros Dellaportas
We present a scalable approach to performing approximate fully Bayesian inference in generic state space models. The proposed method is an alternative to particle MCMC that provide…