98 citations · 196 across the 32 of their papers we have counts for
10 papers · 1 filter
Transformed Samplers with Variance Reduction
Siran Liu, Michalis Tisias, Petros Dellaportas
Markov chain Monte Carlo (MCMC) methods are the standard tool for computing expectations under complex probability distributions. Control variates reduce the variance of the result…
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…
How Good are Low-Rank Approximations in Gaussian Process Regression?
Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos
We provide guarantees for approximate Gaussian Process (GP) regression resulting from two common low-rank kernel approximations: based on random Fourier features, and based on trun…
Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences
Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas
We adopt the interpretability offered by a parametric, Hawkes-process-inspired conditional probability mass function for the marks and apply variational inference techniques to der…
How Good are Low-Rank Approximations in Gaussian Process Regression?
Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos
We provide guarantees for approximate Gaussian Process (GP) regression resulting from two common low-rank kernel approximations: based on random Fourier features, and based on trun…
Gradient-based Adaptive Markov Chain Monte Carlo
Michalis K. Titsias, Petros Dellaportas
We introduce a gradient-based learning method to automatically adapt Markov chain Monte Carlo (MCMC) proposal distributions to intractable targets. We define a maximum entropy regu…