Jan Brauner will speak at the OECD Global Science Forum workshop on “Priority setting and coordination of research agendas: lessons learned from COVID 19”. The workshop will take place on October 4th and 5th, further information and a registration link can be found here.
Jan Brauner and Sören Mindermann have presented their work on the effectiveness of mask-wearing at reducing COVID-19 transmission to the UK Cabinet Office and advised the office on mask-wearing policies.
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.
We’re co-organising the Bayesian Deep Learning Workshop at NeurIPS 2021 as well as two challenges: Approximate Inference in Bayesian Deep Learning and Shifts Challenge: Robustness and Uncertainty under Real-World Distributional Shift.
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. The talk will be made available online.
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.
OATML MSc student Lood van Niekerk received a best poster award at the 2021 ICML Workshop on Computational Biology for his work on “Exploring the latent space of deep generative models: Applications to G-protein coupled receptors”. This is part of an ongoing collaboration between OATML and the Marks Lab.
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.
OATML graduate student Lewis Smith’s and Yarin gave a talk on uncertainty in ML, discussing their collaboration with Adi Hanuka on uncertainty quantification for virtual diagnostics in the SLAC accelerator.
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.
OATML graduate student Lewis Smith’s work as part of FDL 2019 was published as a full length paper in Scientific Reports. This work was a collaboration between the four members of his team, and academics from several universities.
OATML graduate student Pascal Notin will give an invited talk on Uncertainty in deep generative models with applications to genomics and drug design at the Cornell Machine Learning in Medicine seminar series.
OATML graduate student Pascal Notin will give an invited talk on “Large-scale clinical interpretation of genetic variants using evolutionary data and deep generative models” at the EMBL-EBI workshop on Machine Learning in Drug Discovery. The work is a collaboration between OATML and the Marks Lab at Harvard.
OATML graduate student Seb Farquhar will speak on approximate Bayes in large neural networks drawing on Liberty or Depth (NeurIPS 2020) and Radial BNNs (AI Stats 2020) at the UCL Centre for Artificial Intelligence Seminar Series.
Five papers with OATML members accepted to ICLR 2021:
- Invariant Representations for Reinforcement Learning without Reconstruction
- Improving Transformation Invariance in Contrastive Representation Learning
- On Statistical Bias In Active Learning: How and When to Fix It
- Improving VAEs’ Robustness to Adversarial Attack
- Capturing Label Characteristics in VAEs
OATML graduate student Tim G. J. Rudner will give an invited talk on Outcome-Driven Reinforcement Learning via Variational Inference at the MPI+UCLA Mathematical Machine Learning Seminar.
OATML graduate student Tim G. J. Rudner will give an invited talk on “A Non-technical Guide to Modern Machine Learning” at the Georgetown University’s Center for Security & Emerging Technology.
The paper called “Inferring the effectiveness of government interventions against COVID-19” was published in Science today. The work is a collaboration with researchers from 9 universities, led by OATML graduate students Sören Mindermann and Jan Brauner, together with Mrinank Sharma from the Department of Statistics.
OATML hosted a joint workshop with Google Research on reliable machine learning with a series of talks and breakout sessions. The event was organized by OATML graduate student Tim G. J. Rudner and Mario Lučić at Google.
OATML Senior Research Fellow Freddie Kalaitzis will be giving a Spotlight Talk on Dynamic Hydrology Maps from Satellite-LiDAR Fusion at the AI for Earth Sciences workshop at NeurIPS 2020.
OATML graduate students Sören Mindermann and Jan Brauner, together with Mrinank Sharma from the Department of Statistics, were invited to give a talk on their work on ‘inferring the effects of non-pharmaceutical interventions against COVID-19’, at the German Centre for Infection Research/University of Cologne.
OATML DPhil student Angelos Filos is co-organising the NeurIPS 2020 Workshop on Talking to Strangers: Zero-Shot Emergent Communication.
OATML graduate student Lisa Schut presented alongside Rory McGrath, from Accenture Labs, on “Counterfactual Explanations: Making AI decision-making more useful and trustworthy.” The research presented was joint work with Oscar Key, in collaboration with Accenture Labs.
OATML graduate student Clare Lyle will be giving a talk on causal inference and generalization in deep reinforcement learning at the Simons Institute Workshop on Deep Reinforcement Learning on Thursday October 1.
Prof Yarin Gal will give a keynote talk at the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE) workshop at the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference. He will discuss work done at OATML on AI in medical imaging. The talk will cover work on topics ranging from data efficient AI to safe and interpretable AI.
Six papers with OATML members accepted to NeurIPS 2020:
- Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations
- Identifying Causal Effect Inference Failure with Uncertainty-Aware Models
- On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission
- Calibrating Deep Neural Networks using Focal Loss
- A Bayesian Perspective on Training Speed and Model Selection
- Neural Architecture Generator Optimization
OATML graduate student Tim G. J. Rudner will give an invited talk on Inter-domain Deep Gaussian Processes at the UCL Centre for AI’s Statistical Machine Learning Seminar.
Five papers with OATML members accepted to ICML 2020:
- Invariant Causal Prediction for Block MDPs
- Uncertainty Estimation Using a Single Deep Deterministic Neural Network
- Inter-domain Deep Gaussian Processes
- Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
- Can autonomous vehicles identify, recover from, and adapt to distribution shifts?
OATML graduate students Jan Brauner and Sören Mindermann will give an invited talk at Africa CDC, Africa’s intercontinental public health agency, on June 12. They will present their work on nonpharmacetical interventions against COVID-19 to the COVID-19 modelling group.
We’re organising the second “Quantification of Uncertainty in Segmentation” task as part of the Multimodal Brain Tumor Segmentation Challenge at MICCAI 2020, together with Angelos Filos, Raghav Mehta and Tal Arbel.
Our work with NASA and ESA over the past few years, together with Lewis Smith and Tim G. J. Rudner, is summarised in a recent article written by James Parr. Read more in Inspired: Bayesian deep learning for all humankind
Since 1999, MIT Technology Review has recognised young innovators and talented entrepreneurs from different countries who are developing new technologies to help solve the problems that affect our society. Yarin has been included in the ‘pioneers’ category. Read more: AI still makes mistakes, but his tools can alert us when a system is about to go wrong
We are glad to share 25 papers by OATML authors and collaborators presented at NeurIPS 2019 conference and workshops. Read more: blog post
Yarin Gal is one of five new Turing AI Fellows announced by The Alan Turing Institute. The Office for Artificial Intelligence, The Alan Turing Institute and UK Research and Innovation (UKRI) have worked together to successfully attract the Turing AI Fellows, some of the best research talent from around the world. Yarin will work on democratising safe and robust AI. Read more: Turing AI Fellows
Six group members honoured as top reviewers at NeurIPS 2019: 2 members among the top 400 highest scoring reviewers and awarded free registration (Tim G. J. Rudner and Sebastian Farquhar), and 4 among the top 50% reviewers (Zac Kenton, Andreas Kirsch, Angelos Filos and Joost van Amersfoort).
Five papers with OATML members accepted to NeurIPS 2019:
- BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
- A Geometric Perspective on Optimal Representations for Reinforcement Learning
- VIREL: A Variational Inference Framework for Reinforcement Learning
- Variational Bayesian Optimal Experimental Design
- On the Benefits of Disentangled Representations
OATML DPhil student Angelos Filos is co-organising the NeurIPS 2019 Workshop on Emergent Communication: Towards Natural Language.
OATML graduate student Aidan Gomez will be presenting the Recurrent Neural Networks session with Kris Sankaran at Deep Learning Indaba on Tuesday 27 August. Schedule is available here and slides are available here.
We’re organising a “Quantification of Uncertainty in Segmentation” task as part of the Multimodal Brain Tumor Segmentation Challenge at MICCAI 2019, together with Angelos Filos, Raghav Mehta and Tal Arbel.
We’re organising the Fourth Bayesian Deep Learning Workshop at NeurIPS 2019.
Prof Yarin Gal will be speaking at the European Space Agency’s Phi-week, discussing work done at OATML on AI in space. The talk will cover work on topics ranging from exoplanet atmospheric retrieval to asteroid shape modelling from radar data.
OATML graduate student Angelos Filos will present his paper ‘Inverse Reinforcement Learning for Limit Order Book Dynamics’ at the the ICML 2019 Workshop on Applications and Infrastructure for Multi-Agent Learning, with the paper selected for an oral presentation.
Prof Yarin Gal will be speaking at the Royal Society’s Summer Science Exhibition on 6 July, discussing work on AI and art. The talk will cover techniques in machine learning that give us the chance to venture beyond the frame of some famous paintings.
OATML DPhil student Aidan Gomez is co-organising the ICML 2019 Workshop on Invertible Neural Nets and Normalizing Flows together with Aaron Courville and Danilo Rezende.
Prof Yarin Gal will give a talk at the UN summit on “AI for Good” on the development of AI which we can trust: How can we develop trust-worthy machine learning tools to be deployed in the wild? He will further participate in a panel discussion on the topic IT’S A MATTER OF TRUST.
The Publications page has been updated with NeurIPS and UAI papers.
We’re organising the Third Bayesian Deep Learning Workshop at NeurIPS 2018.