Jannik is a DPhil student in the OATML group supervised by Yarin Gal and Tom Rainforth. He is working on (mostly Bayesian) Deep Learning. More specifically, he is currently looking into active learning, semi-supervised learning, and attention. Previously, he has dabbled with structured probabilistic models for object- and physics-aware video prediction. He received an MSc in Physics from Heidelberg University and has spent time studying in Bremen, Padova, and at UCL. Jannik is interested in the societal and ethical implications of AI: He has co-authored a book explaining machine learning to a broad audience, discussed the ethics of AI at the Berlin-Brandenburg Academy of Sciences, and gathered real-world field experience at Bosch.
Publications while at OATML • News items mentioning Jannik Kossen • Reproducibility and Code • Blog Posts
Principled Bayesian deep learning (BDL) does not live up to its potential when we only focus on marginal predictive distributions (marginal predictives). Recent works have highlighted the importance of joint predictives for (Bayesian) sequential decision making from a theoretical and synthetic perspective. We provide additional practical arguments grounded in realworld applications for focusing on joint predictives: we discuss online Bayesian inference, which would allow us to make predictions while taking into account additional data without retraining, and we propose new challenging evaluation settings using active learning and active sampling. These settings are motivated by an examination of marginal and joint predictives, their respective cross-entropies, and their place in offline and online learning. They are more realistic than previously suggested ones, building on work by Wen et al. (2021) and Osband et al. (2022), and focus on evaluating the performance of approximate B... [full abstract]
Andreas Kirsch, Jannik Kossen, Yarin Gal
We propose Active Surrogate Estimators (ASEs), a new method for label-efficient model evaluation. Evaluating model performance is a challenging and important problem when labels are expensive. ASEs address this active testing problem using a surrogate-based estimation approach, whereas previous methods have focused on Monte Carlo estimates. ASEs actively learn the underlying surrogate, and we propose a novel acquisition strategy, XWING, that tailors this learning to the final estimation task. We find that ASEs offer greater label-efficiency than the current state-of-the-art when applied to challenging model evaluation problems for deep neural networks. We further theoretically analyze ASEs' errors.
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input. To this end, we introduce a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoint at a time. Our approach uses self-attention to reason about relationships between datapoints explicitly, which can be seen as realizing non-parametric models using parametric attention mechanisms. However, unlike conventional non-parametric models, we let the model learn end-to-end from the data how to make use of other datapoints for prediction. Empirically, our models solve cross-datapoint lookup and complex reasoning tasks unsolvable by traditional deep learning models. We show highly competitive results on tabular data, early results on CIFAR-10, and give insight into how the model makes use of the interactions between points.
Jannik Kossen, Neil Band, Clare Lyle, Aidan Gomez, Yarin Gal, Tom Rainforth
We introduce active testing: a new framework for sample-efficient model evaluation. While approaches like active learning reduce the number of labels needed for model training, existing literature largely ignores the cost of labeling test data, typically unrealistically assuming large test sets for model evaluation. This creates a disconnect to real applications where test labels are important and just as expensive, e.g. for optimizing hyperparameters. Active testing addresses this by carefully selecting the test points to label, ensuring model evaluation is sample-efficient. To this end, we derive theoretically-grounded and intuitive acquisition strategies that are specifically tailored to the goals of active testing, noting these are distinct to those of active learning. Actively selecting labels introduces a bias; we show how to remove that bias while reducing the variance of the estimator at the same time. Active testing is easy to implement, effective, and can be applied to an... [full abstract]
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
Thirteen papers with OATML members accepted to NeurIPS 2021 main conference. More information in our blog post.
OATML graduate students Sebastian Farquhar and Jannik Kossen receive best reviewer awards (top 10%) at ICML 2021. Further, OATML graduate students Tim G. J. Rudner, Pascal Notin, Panagiotis Tigas, and Binxin Ru have served the conference as expert reviewers.
OATML graduate students Aidan Gomez, Jannik Kossen, and Neil Band will be presenting their recent paper Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning that introduces Non-Parametric Transformers at the Stanford Lecture Course ‘CS25: Transformers United’ on November 1, 2021. Professor Yarin Gal, Dr. Tom Rainforth, and OATML DPhil student Clare Lyle are co-authors on the paper.
The lecture is available online here.
OATML students Jannik Kossen and Neil Band will be presenting their recent paper Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning at Google Research on September 14, 2021. Professor Yarin Gal, Dr. Tom Rainforth, and OATML DPhil students Clare Lyle and Aidan Gomez are co-authors on the paper.
OATML DPhil student Jannik Kossen gives invited talks at AI Campus Berlin on two recent papers: Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning and Active Testing: Sample-Efficient Model Evaluation. Recordings of are available upon request. Announcements are here and here. Professor Yarin Gal, Dr. Tom Rainforth, and OATML graduate students Sebastian Farquhar, Neil Band, Clare Lyle, and Aidan Gomez are co-authors on the papers.
Seven papers with OATML members accepted to ICML 2021, together with 14 workshop papers. More information in our blog post.
OATML students Jannik Kossen and Neil Band present their recent paper Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning at Cohere on July 9, 2021. Professor Yarin Gal, Dr. Tom Rainforth, and OATML DPhil students Clare Lyle and Aidan Gomez are also co-authors on the paper.
OATML group members and collaborators are proud to present 8 papers at NeurIPS 2022 main conference, and 11 workshop papers. …Full post...
Yarin Gal, Freddie Kalaitzis, Sören Mindermann, Lorenz Kuhn, Gunshi Gupta, Jannik Kossen, Pascal Notin, Andrew Jesson, Panagiotis Tigas, Tim G. J. Rudner, Sebastian Farquhar, Ilia Shumailov, 25 Nov 2022
OATML group members and collaborators are proud to present 11 papers at the ICML 2022 main conference and workshops. Group members are also co-organizing the Workshop on Computational Biology, and the Oxford Wom*n Social. …Full post...
Sören Mindermann, Jan Brauner, Muhammed Razzak, Andreas Kirsch, Aidan Gomez, Sebastian Farquhar, Pascal Notin, Tim G. J. Rudner, Freddie Bickford Smith, Neil Band, Panagiotis Tigas, Andrew Jesson, Lars Holdijk, Joost van Amersfoort, Kelsey Doerksen, Jannik Kossen, Yarin Gal, 17 Jul 2022
OATML group members and collaborators are proud to present 13 papers at NeurIPS 2021 main conference. …Full post...
Jannik Kossen, Neil Band, Aidan Gomez, Clare Lyle, Tim G. J. Rudner, Yarin Gal, Binxin (Robin) Ru, Clare Lyle, Lisa Schut, Atılım Güneş Baydin, Tim G. J. Rudner, Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Pascal Notin, Angelos Filos, 11 Oct 2021
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