Generative models and uncertainty quantification lie at the heart of Bayesian modelling and inference. At this small meeting, we discuss recent developments within the field. The meeting is deliberately kept small in order to ensure that discussion remains honest, lively and interesting. Attendance is, thus, mostly by invitation, but one can apply to join (see below).

Confirmed Speakers

Eric Nalisnick

Eric Nalisnick
Assistant Professor, University of Amsterdam.

Yarin Gal

Yarin Gal
Associate Professor, University of Oxford.

Jakub Tomczak

Jakub Tomczak
Assistant professor, Vrije Universiteit Amsterdam.

Clare Lyle

Clare Lyle
PhD student, University of Oxford.

Marine Le Morvan

Marine Le Morvan
Postdoctoral researcher, INRIA Saclay.

Jan-Willem van de Meent

Jan-Willem van de Meent
Associate Professor & Co-director AMLab, University of Amsterdam.

Mark van der Wilk

Mark van der Wilk
Assistant Professor, Imperial College London.

Matthias Bauer

Matthias Bauer
Research Scientist, Deepmind London.

Nicolas Durrande

Nicolas Durrande
Director of Research at Secondmind.

Markus Heinonen

Markus Heinonen
Research Fellow, Aalto University and Finnish Center of AI.

Aude Sportisse

Aude Sportisse
Postdoctoral researcher, INRIA Sophia-Antipolis.


Tuesday (Oct 12) Wednesday (Oct 13)
Session 1
Bayesian deep learning
Chair: Wouter Boomsma
Session 4
Generative models
Chair: Søren Hauberg
9:25-9:30 Opening remarks
Jes Frellsen
9:30-10:30 Matthias Bauer
Revisiting the Laplace Approximation for Model Selection in BNNs
09:00-10:00 Mark van der Wilk
Inductive Biases, Input Densities, and Predictive Uncertainty
10:30-10:50 Coffee 10:00-10:20 Coffee
10:50-11:30 Eric Nalisnick
Predictive Complexity Priors
10:20-11:00 Jan-Willem van de Meent
Compositional Inference in Probabilistic Programs
11:30-12:30 Clare Lyle
Bayesian Model Selection and Generalization in Deep Learning
11:00-12:00 Jakub Tomczak
Is the Likelihood-based Deep Generative Modeling appropriate for Representation Learning?
12:30-13:30 Lunch 12:00-13:00 Lunch
Session 2
Missing data
Chair: Pierre-Alexandre Mattei
Session 5
Chair: Ole Winther
13:30-14:30 Marine Le Morvan
What's a good imputation to predict with missing values?
13:00-14:00 Yarin Gal
Non-Bayesian Nonparametrics
14:30-15:10 Yarin Gal
Surprice talk...
14:00-14:40 Nicolas Durrande
Designing inducing variables that connect sparse Gaussian process models and Neural Networks
15:10-15:30 Coffee 14:40-15:00 Coffee
Session 3
Chair: Jes Frellsen
15:00-15:10 Concluding remark
15:30-16:10 Markus Heinonen
Learning continuous-time dynamics
16:10-17:30 Poster Session
18:00- Conference Dinner
at restaurant Høst (directions)


If you would like to attend this workshop but have yet to receive an invitation, then please write an email to Søren Hauberg and include a link to your website or Google scholar profile. Note that seating is limited, so we cannot guarantee a ticket.


The workshop takes place at:

Ceremonial Hall (Festsalen), University of Copenhagen
Frue Plads 4
1168 København K, Denmark