Talks & organizing


28.6.2018 Markov Chain Importance Sampling BTU Cottbus (slides)
30.5.2018 Markov Chain Importance Sampling Universität Mannheim (slides)
23.3.2018 Highly efficient Bayesian inference with a novel estimator for Metropolis Hastings Zalando Research (slides)
3.7.2017 Stochastic gradient Metropolis-Hastings Monte Carlo Methods 2017
8.6.2017 A highly efficient estimator for Markov Chain algorithms Bayes in Paris, ENSAE
8.3.2017 A Bayesian model for sparse structured sequences FU Berlin Winterseminar (slides)
27.2.2017 Kernel Methods in Machine Learning Zuse Institute Berlin (slides)
8.6.2016 Gradient IS and Unadjusted Langevin for IS Machine Learning group, HU Berlin
25.4.2016 Kernel Sequential Monte Carlo  University of Oxford (slides)
22.4.2016 Kernel Sequential Monte Carlo  University College London (slides)
19.4.2016 Gradient IS and Unadjusted Langevin for IS  University of Reading, Afternoon on Bayesian Computation (slides)
11.3.2016 Gradient Importance Sampling FU Berlin (slides)
3.12.2015 Kernel Adaptive Sequential Monte Carlo Bayes in Paris, ENSAE Paris (slides)
8.6.2015 Bayesian Model Selection for Natural Language Semantics and Iterated Importance Sampling using Gradient Information Computerlinguistisches Kolloquium, Universität Potsdam
16.4.2015 Adaptive Monte Carlo based on Importance Sampling: A gradient-informed algorithm MPI for Intelligent Systems, Tübingen
13.2.2015 Consistency of IS based on dependent sample sets Bayes in Paris, ENSAE Paris
5.11.2014 Probabilistic Models of Natural Language Semantics Korpuslinguistisches Kolloquium, HU Berlin


Stochastic gradient methods for Monte Carlo and variational inference Workshop at MCM 2017
Recent advances in importance sampling Workshop at MCQMC 2016