Talks

21.1.2021	Statistics as linear algebra – distribution embeddings and operators on reproducing kernel Hilbert spaces	Univ. of Bristol (slides)
11.7.2019	Kernel conditional density operators	TU Berlin (slides)
16.8.2018	Tutorial on modern kernel methods	Zalando Research (slides)
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

Organizing

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