2 citations · 3 across the 3 of their papers we have counts for
8 papers
Deterministic Gibbs Sampling via Ordinary Differential Equations
Kirill Neklyudov, Roberto Bondesan, Max Welling
Deterministic dynamics is an essential part of many MCMC algorithms, e.g. Hybrid Monte Carlo or samplers utilizing normalizing flows. This paper presents a general construction of…
Involutive MCMC: a Unifying Framework
Kirill Neklyudov, Max Welling, Evgenii Egorov +1
Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation. The field has developed a broad sp…
The Implicit Metropolis-Hastings Algorithm
Kirill Neklyudov, Evgenii Egorov, Dmitry Vetrov
Recent works propose using the discriminator of a GAN to filter out unrealistic samples of the generator. We generalize these ideas by introducing the implicit Metropolis-Hastings…
MaxEntropy Pursuit Variational Inference
Evgenii Egorov, Kirill Neklydov, Ruslan Kostoev +1
One of the core problems in variational inference is a choice of approximate posterior distribution. It is crucial to trade-off between efficient inference with simple families as…
Metropolis-Hastings view on variational inference and adversarial training
Kirill Neklyudov, Evgenii Egorov, Pavel Shvechikov +1
A significant part of MCMC methods can be considered as the Metropolis-Hastings (MH) algorithm with different proposal distributions. From this point of view, the problem of constr…
Uncertainty Estimation via Stochastic Batch Normalization
Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov +2
In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation. We propose a probabilistic model and show that Batch Normalization maximaze…