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 |