We are very pleased to announce: Laplace's Demon, Bayesian Machine Learning at Scale (BMLS), a seminar series about practical Bayesian methods in academia and industry. https://sites.google.com/view/laplacesdemon/home
The first session of BMLS will be on the 13th of may at 15.00 UTC and will be given by Christian Robert of Université Paris-Dauphine. The second session will be on the 10 June and given by Aki Vehtari of Aalto University. The third session will be on 1 July by John Ormerod of The University of Sydney. We will announce details of other upcoming speakers as they become available, including: Nicolas Chopin, François Caron, Pierre Latouche, Victor Elvira, Sarah Filippi, Chris Oates. Machine learning is changing the world we live in at a break neck pace. From image recognition and generation, to the deployment of recommender systems, it seems to be breaking new ground constantly and influencing almost every aspect of our lives. In this seminar series we ask distinguished speakers to comment on what role Bayesian statistics and Bayesian machine learning have in this rapidly changing landscape. Do we need to optimally process information or borrow strength in the big data era? Are philosophical concepts such as coherence and the likelihood principle relevant when you are running a large scale recommender system? Are variational approximations, MCMC or EP appropriate in a production environment? Can I use the propensity score and call myself a Bayesian? How can I elicit a prior over a massive dataset? Is Bayes a reasonable theory of how to be perfect but a hopeless theory of how to be good? Do we need Bayes when we can just A/B test? What combinations of pragmatism and idealism can be used to deploy Bayesian machine learning in a large scale live system? We ask Bayesian believers, Bayesian pragmatists and Bayesian sceptics to comment on all of these subjects and more. Please stay informed on the series by joining the Google group or following us on Twitter. Registration for Christian Robert's 13th May talk 'Component-wise approximate Bayesian computation via Gibbs-like steps' is already open. https://sites.google.com/view/laplacesdemon/home
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