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).


Tim Salimans

Tim Salimans
Machine Learning research scientist, Google Brain.

Vincent Dutordoir

Vincent Dutordoir
PhD Candidate, University of Cambridge.

Yingzhen Li

Yingzhen Li
Lecturer, Imperial College London.

Andrew Gordon Wilson

Andrew Gordon Wilson
Associate Professor, Courant Institute of Mathematical Sciences, New York University.

Brooks Paige

Brooks Paige
Associate professor, University College London.

James Hensman

James Hensman
Principal researcher, Microsoft Research Cambridge.

Carl Henrik Ek

Carl Henrik Ek
Associate professor, University of Cambridge.

Jan-Willem van de Meent

Philipp Hennig
Professor, University of Tübingen.

Emiel Hoogeboom

Emiel Hoogeboom
PhD student, University of Amsterdam.

Cheng Zhang

Cheng Zhang
Principal researcher, Microsoft Research Cambridge.

Antoine Wehenkel

Antoine Wehenkel
PhD student, University of Liège.

Benjamin Bloem-Reddy

Benjamin Bloem-Reddy
Assistant professor, University of British Columbia.

Arno Solin

Arno Solin
Assistant Professor, Aalto University.


Wednesday (Sep 14) Thursday (Sep 15)
Session 1
Learning with limited supervision
Chair: Jes Frellsen
Session 5
Diffusion models
Chair: Ole Winther
8:55-9:00 Opening remarks
Jes Frellsen
9:00-10:00 Yingzhen Li
Understanding masked pre-training: fundamentally different from "old-fashioned" unsupervised learning?
9:00-10:00 Emiel Hoogeboom
Score-based diffusion: how did we get here and open problems
10:00-10:40 Brooks Paige
Active learning with semi-supervised learners
10:00-10:40 Vincent Dutordoir
Neural Diffusion Processes
10:40-11:00 Coffee 10:40-11:00 Coffee
Session 2
Chair: Søren Hauberg
11:00-12:00 Benjamin Bloem-Reddy
Why (and when) should we care about identifiability in generative models?
11:00-12:00 Tim Salimans
Imagen: diffusion models for text2image and beyond
12:00-13:00 James Hensman
Orthogonality and identifiability in probabilistic models
12:00-13:00 Arno Solin
Generative modelling with inverse heat dissipation
13:00-14:00 Lunch 13:00-14:00 Lunch
Session 3
Statistical and causal inference
Chair: Pierre-Alexandre Mattei
Session 6
Scientific applications of generative models
Chair: Wouter Boomsma
14:00-15:00 Cheng Zhang
Causal Decision Making under Uncertainty
14:00-15:00 Antoine Wehenkel
The symbiosis between deep probabilistic and scientific models
15:00-16:00 Philipp Hennig
Inference through simulations with Differential Equation Filters
15:00-16:00 Coffee and concluding remarks
16:00-16:20 Coffee
Session 4
Scientific applications of generative models
Chair: Wouter Boomsma
16:20-17:20 Carl Henrik Ek
Generative Model for Sequential Alignment
17:20-18:50 Poster Session
19:00- Workshop Dinner
at restaurant Carl's Øl og Spisehus (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:

Carlsberg Akademi
Gamle Carlsberg Vej 15
1799 Copenhagen V
Venue website

Pioneer Centre for AI
Univeristy of Copenhagen
Technical University of Denmark