Andreas Kirsch

Department of Computer Science

University of Oxford

Wolfson Building

Oxford, OX1 3QD

I am a DPhil student with Prof Yarin Gal in the OATML group at the University of Oxford and a student in the AIMS program.

Originally from Romania, I grew up in Southern Germany. After studying Computer Science and Mathematics at the Technical University in Munich, I spent a couple of years in Zurich as a software engineer at Google (YouTube). I have worked as a performance research engineer at DeepMind for a year before starting my DPhil in September 2018.

I am interested in Bayesian Deep Learning, Information Theory (and its application within Information Bottlenecks and Active Learning), and Uncertainty Quantification. I also like to think about AI Ethics and AI Safety.

selected publications

Preprint

Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty

Mukhoti, Jishnu, Kirsch, Andreas, van Amersfoort, Joost, Torr, Philip HS, and Gal, Yarin

arXiv Preprint 2021

ICML UDL 2020

Learning CIFAR-10 with a Simple Entropy Estimator Using Information Bottleneck Objectives

Kirsch, Andreas, Lyle, Clare, and Gal, Yarin

In Workshop Uncertainty & Robustness in Deep Learning at Int. Conf. on Machine Learning (ICML), online 2020

Preprint

Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning

Kirsch, Andreas, Lyle, Clare, and Gal, Yarin

arXiv Preprint 2020

NeurIPS 2019

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

Kirsch, Andreas, van Amersfoort, Joost, and Gal, Yarin

NeurIPS 2019

news

Feb 23, 2021	Lecture on “Bayesian Deep Learning, Information Theory and Active Learning” for Oxford Global Exchanges. You can download the slides here.
Feb 21, 2021	Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty has been uploaded to arXiv as pre-print. Joint work with Jishnu Mukhoti, and together with Joost van Amersfoort, Philip H.S. Torr, Yarin Gal. We show that a single softmax neural net with minimal changes can beat the uncertainty predictions of Deep Ensembles and other more complex single-forward-pass uncertainty approaches.
Dec 10, 2020	Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning was also presented as a poster at the “NeurIPS Europe meetup on Bayesian Deep Learning”. You can find the poster below (click to open): or as PDF version to download.
Jul 17, 2020	Two workshop papers have been accepted to Uncertainty & Robustness in Deep Learning Workshop at ICML 2020: Scalable Training with Information Bottleneck Objectives, and Learning CIFAR-10 with a Simple Entropy Estimator Using Information Bottleneck Objectives both together with Clare Lyle and Yarin Gal. They are based on Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning for the former, and an application of the UIB framework for the latter: we can use it to train models that perform well on CIFAR-10 without using a cross-entropy loss at all.
Mar 27, 2020	Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning, together with Clare Lyle and Yarin Gal, has been uploaded as pre-print to arXiv. It examines and unifies different Information Bottleneck objectives and shows that we can introduce simple yet effective surrogate objectives without complex derivations.
Sep 4, 2019	BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning got accepted into NeurIPS 2019. See you all in Vancouver!
Jun 24, 2019	BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning has been uploaded to arXiv, and we have also published an extensive blog post about it on OATML with the code available on GitHub.

Feb 23, 2021

Lecture on “Bayesian Deep Learning, Information Theory and Active Learning” for Oxford Global Exchanges. You can download the slides here.

Feb 21, 2021

Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty has been uploaded to arXiv as pre-print. Joint work with Jishnu Mukhoti, and together with Joost van Amersfoort, Philip H.S. Torr, Yarin Gal. We show that a single softmax neural net with minimal changes can beat the uncertainty predictions of Deep Ensembles and other more complex single-forward-pass uncertainty approaches.

Dec 10, 2020

Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning was also presented as a poster at the “NeurIPS Europe meetup on Bayesian Deep Learning”.

You can find the poster below (click to open):

or as PDF version to download.

Jul 17, 2020

Two workshop papers have been accepted to Uncertainty & Robustness in Deep Learning Workshop at ICML 2020:

both together with Clare Lyle and Yarin Gal. They are based on Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning for the former, and an application of the UIB framework for the latter: we can use it to train models that perform well on CIFAR-10 without using a cross-entropy loss at all.

Mar 27, 2020

Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning, together with Clare Lyle and Yarin Gal, has been uploaded as pre-print to arXiv. It examines and unifies different Information Bottleneck objectives and shows that we can introduce simple yet effective surrogate objectives without complex derivations.

Sep 4, 2019

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning got accepted into NeurIPS 2019. See you all in Vancouver!

Jun 24, 2019

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning has been uploaded to arXiv, and we have also published an extensive blog post about it on OATML with the code available on GitHub.