Joost is a DPhil student in the OATML group in the Department of Computer Science at the University of Oxford, supervised by Yarin Gal and Yee Whye Teh. He is interested in uncertainty estimation, representation learning, variational inference, and its applications to for example active learning. Previously, he spent two years as a Research Engineer at Twitter’s machine learning team Cortex as part of the team that came out of the Magic Pony acquisition. He obtained his MSc. at the University of Amsterdam, working with Max Welling and Diederik Kingma. He is an Oxford-Google DeepMind scholar.
We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. Our approach, deterministic uncertainty quantification (DUQ), builds upon ideas of RBF networks. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models. By enforcing detectability of changes in the input using a gradient penalty, we are able to reliably detect out of distribution data. Our uncertainty quantification scales well to large datasets, and using a single model, we improve upon or match Deep Ensembles in out of distribution detection on notable difficult dataset pairs such as FashionMNIST vs. MNIST, and CIFAR-10 vs. SVHN.
Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the task of deep Bayesian active learning. BatchBALD is a greedy linear-time 1−1/e-approximate algorithm amenable to dynamic programming and efficient caching. We compare BatchBALD to the commonly used approach for batch data acquisition and find that the current approach acquires similar and redundant points, sometimes performing worse than randomly acquiring data. We finish by showing that, using BatchBALD to consider dependencies within an acquisition batch, we achieve new state of the art performance on standard benchmarks, providing substantial data efficiency improvements in batch acquisition.
Andreas Kirsch, Joost van Amersfoort, Yarin Gal
Many labs have converged on using Slurm for managing their shared compute resources. It is fairly easy to get going with Slurm, but it quickly gets unintuitive when wanting to run a hyper-parameter search. In this repo, Joost van Amersfoort provides some scripts to make starting many jobs painless and easy to control.Code
Joost van Amersfoort
This is the code for the blog post Human in the Loop: Deep Learning without Wasteful Labelling and the paper BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.Code, Publication
Andreas Kirsch, Joost van Amersfoort, Yarin Gal
OATML group members and collaborators are proud to present 21 papers at ICML 2021, including 7 papers at the main conference and 14 papers at various workshops. Group members will also be giving invited talks and participate in panel discussions at the workshops. …Full post...
Angelos Filos, Clare Lyle, Jannik Kossen, Sebastian Farquhar, Tom Rainforth, Andrew Jesson, Sören Mindermann, Tim G. J. Rudner, Oscar Key, Binxin (Robin) Ru, Pascal Notin, Panagiotis Tigas, Andreas Kirsch, Jishnu Mukhoti, Joost van Amersfoort, Lisa Schut, Muhammed Razzak, Aidan Gomez, Jan Brauner, Yarin Gal, 17 Jul 2021
We are glad to share the following 13 papers by OATML authors and collaborators to be presented at this ICML conference and workshops …Full post...
Angelos Filos, Sebastian Farquhar, Tim G. J. Rudner, Lewis Smith, Lisa Schut, Tom Rainforth, Panagiotis Tigas, Pascal Notin, Andreas Kirsch, Clare Lyle, Joost van Amersfoort, Jishnu Mukhoti, Yarin Gal, 10 Jul 2020
We are glad to share the following 25 papers by OATML authors and collaborators to be presented at this NeurIPS conference and workshops. …Full post...
Angelos Filos, Sebastian Farquhar, Aidan Gomez, Tim G. J. Rudner, Zac Kenton, Lewis Smith, Milad Alizadeh, Tom Rainforth, Panagiotis Tigas, Andreas Kirsch, Clare Lyle, Joost van Amersfoort, Yarin Gal, 08 Dec 2019
In Active Learning we use a “human in the loop” approach to data labelling, reducing the amount of data that needs to be labelled drastically, and making machine learning applicable when labelling costs would be too high otherwise. In our paper  we present BatchBALD: a new practical method for choosing batches of informative points in Deep Active Learning which avoids labelling redundancies that plague existing methods. Our approach is based on information theory and expands on useful intuitions. We have also made our implementation available on GitHub at https://github.com/BlackHC/BatchBALD. …Full post...
Andreas Kirsch, Joost van Amersfoort, Yarin Gal, 24 Jun 2019