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).
Professor in Machine Learning at the University of Amsterdam and a VP Technologies at Qualcomm.
Associate professor in computational probabilistic modeling at Aalto University; visiting professor at Technical University of Denmark (DTU).
PhD student at the University of Toronto.
Mark van der Wilk
Machine Learning Researcher PROWLER.io.
José Miguel Hernández Lobato
University Lecturer in Machine Learning, University of Cambridge.
Rianne van den Berg
Research Scientist at Google AI, Amsterdam.
Machine learning researcher in the Brain team at Google AI, Berlin.
Lecturer of Computer Science at Universidad Autónoma de Madrid.
Research scientist at DeepMind.
Researcher in statistical machine learning at the University of Oxford.
Casper Kaae Sønderby
Research Scientist in Google Brains Amsterdam Lab.
Professor of Statistics at Ecole Polytechnique (CMAP) and XPOP INRIA team.
Staff Research Scientist in Machine Learning at DeepMind.
|Wednesday (Oct 9)||Thursday (Oct 10)|
Advances in deep generative models
Chair: Søren Hauberg
Bayesian neural networks
Chair: Wouter Boomsma
The Functional Process VAE
Practical Bayesian Neural Networks: Three Obstacles
Resampled Priors for Variational Autoencoders
|09:40-10:40||Casper Kaae Sønderby
Bayesian Inference for Large Scale Image Classification
|11:00-11:40||Rianne van den Berg
Normalizing flows for discrete data
|Session 4 |
Generative models for downstream tasks
Chair: Ole Winther
Disentangling Disentanglement in Variational Autoencoders
|11:00-12:00||José Miguel Hernández-Lobato
Advances in Compression via Probabilistic Machine Learning
Bayesian and causal inference
Chair: Pierre-Alexandre Mattei
|12:40-13:40||Lunch||13:00-14:00||Mark van der Wilk
Sampling for data augmentation: generative models vs invariances
The many virtues of Incorporating energy-based generative models into discriminative learning
Adversarial Alpha Divergence Minimization for Bayesian Approximate Inference
Causal inference and fairness
at Toldboden (directions)
The workshop takes place at:
The conference is jointly organized by:
We are grateful for funding from the Villum Foundation (grant 15334) and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement number 757360).