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Tim G. J. Rudner

PhD, started 2017

Tim is a DPhil student in the Department of Computer Science at the University of Oxford, working with Yarin Gal and Yee Whye Teh. His research interests span variational inference, reinforcement learning, Bayesian deep learning, and AI safety. Tim obtained a master’s degree in statistics from the University of Oxford and an undergraduate degree in mathematics and economics from Yale University, where he received the Charles E. Clark Memorial Award for Academic Excellence. During his DPhil, Tim was a Visiting Fellow at the University of California, Berkeley and Yale University. He is also an AI Fellow at Georgetown University’s Center for Security and Emerging Technology, a Fellow of the German Academic Scholarship Foundation, recipient of the Qualcomm Innovation Fellowship, and a Rhodes Scholar.


Publications while at OATMLNews items mentioning Tim G. J. RudnerReproducibility and CodeBlog Posts

Publications while at OATML:

On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations

KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks. However, we show that KL-regularized reinforcement learning with behavioral policies derived from expert demonstrations suffers from hitherto unrecognized pathological behavior that can lead to slow, unstable, and suboptimal online training. We show empirically that the pathology occurs for commonly chosen behavioral policy classes and demonstrate its impact on sample efficiency and online policy performance. Finally, we show that the pathology can be remedied by specifying non-parametric behavioral policies and that doing so allows KL-regularized RL to significantly outperform state-of-the-art approaches on a variety of challenging locomotion and dexterous hand manipulation tasks.


Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
NeurIPS, 2021
ICLR Workshop on Robust and Reliable Machine Learning in the Real World, 2021
[OpenReview] [Website] [BibTex]

Outcome-Driven Reinforcement Learning via Variational Inference

While reinforcement learning algorithms provide automated acquisition of optimal policies, practical application of such methods requires a number of design decisions, such as manually designing reward functions that not only define the task, but also provide sufficient shaping to accomplish it. In this paper, we view reinforcement learning as inferring policies that achieve desired outcomes, rather than as a problem of maximizing rewards. To solve this inference problem, we establish a novel variational inference formulation that allows us to derive a well-shaped reward function which can be learned directly from environment interactions. From the corresponding variational objective, we also derive a new probabilistic Bellman backup operator and use it to develop an off-policy algorithm to solve goal-directed tasks. We empirically demonstrate that this method eliminates the need to hand-craft reward functions for a suite of diverse manipulation and locomotion tasks and leads to ef... [full abstract]


Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister, Yarin Gal, Sergey Levine
NeurIPS, 2021
NeurIPS Workshop on Deep Reinforcement Learning, 2020
[arXiv] [OpenReview] [BibTex]

Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks

Bayesian deep learning seeks to equip deep neural networks with the ability to precisely quantify their predictive uncertainty, and has promised to make deep learning more reliable for safety-critical real-world applications. Yet, existing Bayesian deep learning methods fall short of this promise; new methods continue to be evaluated on unrealistic test beds that do not reflect the complexities of downstream real-world tasks that would benefit most from reliable uncertainty quantification. We propose a set of real-world tasks that accurately reflect such complexities and are designed to assess the reliability of predictive models in safety-critical scenarios. Specifically, we curate two publicly available datasets of high-resolution human retina images exhibiting varying degrees of diabetic retinopathy, a medical condition that can lead to blindness, and use them to design a suite of automated diagnosis tasks that require reliable predictive uncertainty quantification. We use these... [full abstract]


Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal
NeurIPS Datasets and Benchmarks Track, 2021
Spotlight Talk, NeurIPS Workshop on Distribution Shifts, 2021
Symposium on Machine Learning for Health (ML4H) Extended Abstract Track, 2021
NeurIPS Workshop on Bayesian Deep Learning, 2021
[OpenReview] [Code] [BibTex]

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning

High-quality estimates of uncertainty and robustness are crucial for numerous real-world applications, especially for deep learning which underlies many deployed ML systems. The ability to compare techniques for improving these estimates is therefore very important for research and practice alike. Yet, competitive comparisons of methods are often lacking due to a range of reasons, including: compute availability for extensive tuning, incorporation of sufficiently many baselines, and concrete documentation for reproducibility. In this paper we introduce Uncertainty Baselines: high-quality implementations of standard and state-of-the-art deep learning methods on a variety of tasks. As of this writing, the collection spans 19 methods across 9 tasks, each with at least 5 metrics. Each baseline is a self-contained experiment pipeline with easily reusable and extendable components. Our goal is to provide immediate starting points for experimentation with new methods or applications. Addi... [full abstract]


Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran
NeurIPS Workshop on Bayesian Deep Learning, 2021
[arXiv] [Code] [Blog Post (Google AI)] [BibTex]

Tractable Function-Space Variational Inference in Bayesian Neural Networks

The most common approach to inference in Bayesian neural networks is to approximate the posterior distribution over the network parameters. However, explicit inference over the network parameters can make it difficult to incorporate meaningful prior information about the prediction task into the inference process. In this paper, we consider an alternative approach. Taking advantage of the fact that Bayesian neural networks define distributions over functions induced by distributions over parameters, we propose a scalable and tractable method for function-space variational inference. We evaluate the proposed method empirically and show that it leads to competitive predictive accuracy and reliable predictive uncertainty estimation on a range of prediction problems and performs well on safety-critical downstream tasks where reliable uncertainty estimation is essential.


Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal
Symposium on Advances in Approximate Bayesian Inference, 2021
ICML Workshop on Uncertainty & Robustness in Deep Learning, 2021
[Preprint] [BibTex]

Continual Learning via Function-Space Variational Inference

Continual learning is the process of developing new abilities while retaining existing ones. Sequential Bayesian inference over predictive functions is a natural framework for doing this, but applying it to deep neural networks is challenging in practice. To address the drawbacks of existing approaches, we formulate continual learning as sequential function-space variational inference. From this formulation, we derive a tractable variational objective that explicitly encourages a neural network to fit data from new tasks while also matching posterior distributions over functions inferred from previous tasks. Crucially, the objective is expressed purely in terms of predictive functions. This way, the parameters of the neural network can vary widely during training, allowing easier adaptation to new tasks than is possible with techniques that directly regularize parameters. We demonstrate that, across a range of task sequences, neural networks trained via sequential function-space va... [full abstract]


Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal
ICML Workshop on Theory and Foundations of Continual Learning, 2021
ICML Workshop on Subset Selection in Machine Learning, From Theory to Applications, 2021
[Preprint] [BibTex]

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes

We show that the gradient estimates used in training Deep Gaussian Processes (DGPs) with importance-weighted variational inference are susceptible to signal-to-noise ratio (SNR) issues. Specifically, we show both theoretically and empirically that the SNR of the gradient estimates for the latent variable's variational parameters decreases as the number of importance samples increases. As a result, these gradient estimates degrade to pure noise if the number of importance samples is too large. To address this pathology, we show how doubly-reparameterized gradient estimators, originally proposed for training variational autoencoders, can be adapted to the DGP setting and that the resultant estimators completely remedy the SNR issue, thereby providing more reliable training. Finally, we demonstrate that our fix can lead to improvements in the predictive performance of the model's predictive posterior.


Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
ICML, 2021
[arXiv] [Code] [BibTex]

Inter-domain Deep Gaussian Processes

Inter-domain Gaussian processes (GPs) allow for high flexibility and low computational cost when performing approximate inference in GP models. They are particularly suitable for modeling data exhibiting global structure but are limited to stationary covariance functions and thus fail to model non-stationary data effectively. We propose Inter-domain Deep Gaussian Processes, an extension of inter-domain shallow GPs that combines the advantages of inter-domain and deep Gaussian processes (DGPs), and demonstrate how to leverage existing approximate inference methods to perform simple and scalable approximate inference using inter-domain features in DGPs. We assess the performance of our method on a range of regression tasks and demonstrate that it outperforms inter-domain shallow GPs and conventional DGPs on challenging large-scale real-world datasets exhibiting both global structure as well as a high-degree of non-stationarity.


Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
ICML, 2020
[arXiv] [Website] [Talk] [Slides] [BibTex]

The Natural Neural Tangent Kernel: Neural Network Training Dynamics under Natural Gradient Descent

Gradient-based optimization methods have proven successful in learning complex, overparameterized neural networks from non-convex objectives. Yet, the precise theoretical relationship between gradient-based optimization methods, the resulting training dynamics, and generalization in deep neural networks (DNNs) remains unclear. In this work, we investigate the training dynamics of overparameterized DNNs of \emph{finite-width} under natural gradient descent. To do so, we take a function-space view of the training dynamics under natural gradient descent and derive a bound on the discrepancy between the DNN predictive distributions induced by linearized and non-linearized natural gradient descent. Unlike prior work, our bound quantifies the extent to which linearization of the training dynamics of finite-width DNNs affects DNN predictions on arbitrary test points.


Tim G. J. Rudner, Florian Wenzel, Yee Whye Teh, Yarin Gal
Contributed talk, NeurIPS Workshop on Bayesian Deep Learning, 2019
[Preprint]

A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks

Evaluation of Bayesian deep learning (BDL) methods is challenging. We often seek to evaluate the methods' robustness and scalability, assessing whether new tools give 'better' uncertainty estimates than old ones. These evaluations are paramount for practitioners when choosing BDL tools on-top of which they build their applications. Current popular evaluations of BDL methods, such as the UCI experiments, are lacking: Methods that excel with these experiments often fail when used in application such as medical or automotive, suggesting a pertinent need for new benchmarks in the field. We propose a new BDL benchmark with a diverse set of tasks, inspired by a real-world medical imaging application on diabetic retinopathy diagnosis. Visual inputs (512x512 RGB images of retinas) are considered, where model uncertainty is used for medical pre-screening---i.e. to refer patients to an expert when model diagnosis is uncertain. Methods are then ranked according to metrics derived from expert-... [full abstract]


Angelos Filos, Sebastian Farquhar, Aidan Gomez, Tim G. J. Rudner, Zac Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, Yarin Gal
Spotlight talk, NeurIPS Workshop on Bayesian Deep Learning, 2019
[Preprint] [Code] [BibTex]

VIREL: A Variational Inference Framework for Reinforcement Learning

Applying probabilistic models to reinforcement learning (RL) enables the application of powerful optimisation tools such as variational inference to RL. However, existing inference frameworks and their algorithms pose significant challenges for learning optimal policies, e.g., the absence of mode capturing behaviour in pseudo-likelihood methods and difficulties learning deterministic policies in maximum entropy RL based approaches. We propose VIREL, a novel, theoretically grounded probabilistic inference framework for RL that utilises a parametrised action-value function to summarise future dynamics of the underlying MDP. This gives VIREL a mode-seeking form of KL divergence, the ability to learn deterministic optimal polices naturally from inference and the ability to optimise value functions and policies in separate, iterative steps. In applying variational expectation-maximisation to VIREL we thus show that the actor-critic algorithm can be reduced to expectation-maximisation, w... [full abstract]


Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
NeurIPS, 2019
NeurIPS 2018 Workshop on Probabilistic Reinforcement Learning and Structured Control
[arXiv] [BibTex]

The StarCraft Multi-Agent Challenge

In the last few years, deep multi-agent reinforcement learning (RL) has become a highly active area of research. A particularly challenging class of problems in this area is partially observable, cooperative, multi-agent learning, in which teams of agents must learn to coordinate their behaviour while conditioning only on their private observations. This is an attractive research area since such problems are relevant to a large number of real-world systems and are also more amenable to evaluation than general-sum problems. Standardised environments such as the ALE and MuJoCo have allowed single-agent RL to move beyond toy domains, such as grid worlds. However, there is no comparable benchmark for cooperative multi-agent RL. As a result, most papers in this field use one-off toy problems, making it difficult to measure real progress. In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap. SMAC is based on the popular real-time st... [full abstract]


Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob Foerster, Shimon Whiteson
AAMAS 2019
NeurIPS 2019 Workshop on Deep Reinforcement Learning
[arXiv] [Code] [BibTex] [Media]

Multi³Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery

We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network. Our model significantly expedites the generation of satellite imagery-based flood maps, crucial for first responders and local authorities in the early stages of flood events. By incorporating multitemporal satellite imagery, our model allows for rapid and accurate post-disaster damage assessment and can be used by governments to better coordinate medium- and long-term financial assistance programs for affected areas. The network consists of multiple streams of encoder-decoder architectures that extract spatiotemporal information from medium-resolution images and spatial information from high-resolution images before fusing the resulting representations into a single medium-resolution segmentation map of flooded buildings. We compare our model to state-of-the-art methods for building footprint segmenta... [full abstract]


Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopackova, Piotr Bilinski
AAAI 2019
NeurIPS 2018 Workshop AI for Social Good
[arXiv] [Code] [BibTex] [Media]

On the Connection between Neural Processes and Gaussian Processes with Deep Kernels

Neural Processes (NPs) are a class of neural latent variable models that combine desirable properties of Gaussian Processes (GPs) and neural networks. Like GPs, NPs define distributions over functions and are able to estimate the uncertainty in their predictions. Like neural networks, NPs are computationally efficient during training and prediction time. We establish a simple and explicit connection between NPs and GPs. In particular, we show that, under certain conditions, NPs are mathematically equivalent to GPs with deep kernels. This result further elucidates the relationship between GPs and NPs and makes previously derived theoretical insights about GPs applicable to NPs. Furthermore, it suggests a novel approach to learning expressive GP covariance functions applicable across different prediction tasks by training a deep kernel GP on a set of datasets


Tim G. J. Rudner, Vincent Fortuin, Yee Whye Teh, Yarin Gal
NeurIPS Workshop on Bayesian Deep Learning, 2018
[Paper] [BibTex]
More publications on Google Scholar.

News items mentioning Tim G. J. Rudner:

NeurIPS 2021

NeurIPS 2021

11 Oct 2021

Thirteen papers with OATML members accepted to NeurIPS 2021 main conference. More information in our blog post.

Link to this news item
OATML graduate students receive best reviewer awards and serve as expert reviewers at ICML 2021

OATML graduate students receive best reviewer awards and serve as expert reviewers at ICML 2021

06 Sep 2021

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.

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ICML 2021

ICML 2021

17 Jul 2021

Seven papers with OATML members accepted to ICML 2021, together with 14 workshop papers. More information in our blog post.

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OATML graduate students receive Outstanding Reviewer Awards

OATML graduate students receive Outstanding Reviewer Awards

03 Jun 2021

OATML graduate students Pascal Notin and Tim G. J. Rudner received best reviewer awards (top 5%) at UAI 2021.

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Tim G. J. Rudner awarded Qualcomm Innovation Fellowship

Tim G. J. Rudner awarded Qualcomm Innovation Fellowship

19 May 2021

Tim G. J. Rudner is one of 4 PhD students awarded the 2021 Qualcomm Innovation Fellowship Europe. Congratulations to the winners!

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Tim G. J. Rudner releases paper series on AI safety

Tim G. J. Rudner releases paper series on AI safety

17 Mar 2021

OATML graduate student Tim G. J. Rudner wrote a paper series on AI safety for non-experts (An Overview, Robustness, Interpretability).

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OATML researcher to speak at Max Planck Institute & UCLA

OATML researcher to speak at Max Planck Institute & UCLA

17 Dec 2020

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. Professor Yarin Gal and collaborator Sergey Levine are also co-authors on the paper.

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Tim G. J. Rudner to speak at Center for Security & Emerging Technology

Tim G. J. Rudner to speak at Center for Security & Emerging Technology

16 Dec 2020

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.

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Tim G. J. Rudner to give guest lecture at UCL

Tim G. J. Rudner to give guest lecture at UCL

16 Dec 2020

OATML graduate student Tim G. J. Rudner will give an invited guest lecture on Bayesian Deep Learning at University College London.

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Oxford-Google Workshop on Reliable Machine Learning

Oxford-Google Workshop on Reliable Machine Learning

24 Nov 2020

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.

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Tim G. J. Rudner receives Outstanding Reviewer Award

Tim G. J. Rudner receives Outstanding Reviewer Award

01 Nov 2020

OATML graduate student Tim G. J. Rudner received a best reviewer award (top 10%) at NeurIPS 2020.

Link to this news item
OATML student to speak at University of Cambridge

OATML student to speak at University of Cambridge

16 Oct 2020

OATML graduate student Tim G. J. Rudner will give an invited talk about his work with Professor Yarin Gal on Inter-domain Deep Gaussian Processes at the University of Cambridge’s ML@CS Seminar.

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OATML researcher invited to join OECD AI Working Groups

OATML researcher invited to join OECD AI Working Groups

01 Oct 2020

OATML graduate student Tim G. J. Rudner has joined the OECD’s Working Groups on Trustworthy AI and AI Classification as an invited expert.

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Tim G. J. Rudner to speak at UCL Centre for AI

Tim G. J. Rudner to speak at UCL Centre for AI

13 Aug 2020

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.

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Bayesian deep learning for all humankind

Bayesian deep learning for all humankind

14 Jan 2020

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

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Six group members honoured as NeurIPS top reviewers

Six group members honoured as NeurIPS top reviewers

07 Sep 2019

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).

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OATML students received ICML 2019 Outstanding Reviewer Awards

OATML students received ICML 2019 Outstanding Reviewer Awards

02 Jun 2019

OATML DPhil students Lewis Smith and Tim G. J. Rudner received ICML 2019 Outstanding Reviewer Awards (top 5% of reviewers).

Link to this news item


Reproducibility and Code

Code for Bayesian Deep Learning Benchmarks

In order to make real-world difference with Bayesian Deep Learning (BDL) tools, the tools must scale to real-world settings. And for that we, the research community, must be able to evaluate our inference tools (and iterate quickly) with real-world benchmark tasks. We should be able to do this without necessarily worrying about application-specific domain knowledge, like the expertise often required in medical applications for example. We require benchmarks to test for inference robustness, performance, and accuracy, in addition to cost and effort of development. These benchmarks should be at a variety of scales, ranging from toy MNIST-scale benchmarks for fast development cycles, to large data benchmarks which are truthful to real-world applications, capturing their constraints.

Code
Angelos Filos, Sebastian Farquhar, Aidan Gomez, Tim G. J. Rudner, Zac Kenton, Lewis Smith, Milad Alizadeh, Yarin Gal

Code for Multi³Net (multitemporal satellite imagery segmentation)

We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network. Our model significantly expedites the generation of satellite imagery-based flood maps, crucial for first responders and local authorities in the early stages of flood events. By incorporating multitemporal satellite imagery, our model allows for rapid and accurate post-disaster damage assessment and can be used by governments to better coordinate medium- and long-term financial assistance programs for affected areas. The network consists of multiple streams of encoder-decoder architectures that extract spatiotemporal information from medium-resolution images and spatial information from high-resolution images before fusing the resulting representations into a single medium-resolution segmentation map of flooded buildings. We compare our model to state-of-the-art methods for building footprint segmentation as well as to alternative fusion approaches for the segmentation of flooded buildings and find that our model performs best on both tasks. We also demonstrate that our model produces highly accurate segmentation maps of flooded buildings using only publicly available medium-resolution data instead of significantly more detailed but sparsely available very high-resolution data. We release the first open-source dataset of fully preprocessed and labeled multiresolution, multispectral, and multitemporal satellite images of disaster sites along with our source code.

Code, Publication
Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopackova, Piotr Bilinski


Blog Posts

OATML at ICML 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. …

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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

13 OATML Conference papers at NeurIPS 2021

OATML group members and collaborators are proud to present 13 papers at NeurIPS 2021 main conference. …

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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

21 OATML Conference and Workshop papers at ICML 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. …

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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

22 OATML Conference and Workshop papers at NeurIPS 2020

OATML group members and collaborators are proud to be presenting 22 papers at NeurIPS 2020. Group members are also co-organising various events around NeurIPS, including workshops, the NeurIPS Meet-Up on Bayesian Deep Learning and socials. …

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Muhammed Razzak, Panagiotis Tigas, Angelos Filos, Atılım Güneş Baydin, Andrew Jesson, Andreas Kirsch, Clare Lyle, Freddie Kalaitzis, Jan Brauner, Jishnu Mukhoti, Lewis Smith, Lisa Schut, Mizu Nishikawa-Toomey, Oscar Key, Binxin (Robin) Ru, Sebastian Farquhar, Sören Mindermann, Tim G. J. Rudner, Yarin Gal, 04 Dec 2020

13 OATML Conference and Workshop papers at ICML 2020

We are glad to share the following 13 papers by OATML authors and collaborators to be presented at this ICML conference and workshops …

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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

25 OATML Conference and Workshop papers at NeurIPS 2019

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

Bayesian Deep Learning Benchmarks

In order to make real-world difference with Bayesian Deep Learning (BDL) tools, the tools must scale to real-world settings. And for that we, the research community, must be able to evaluate our inference tools (and iterate quickly) with real-world benchmark tasks. We should be able to do this without necessarily worrying about application-specific domain knowledge, like the expertise often required in medical applications for example. We require benchmarks to test for inference robustness, performance, and accuracy, in addition to cost and effort of development. These benchmarks should be at a variety of scales, ranging from toy MNIST-scale benchmarks for fast development cycles, to large data benchmarks which are truthful to real-world applications, capturing their constraints. …

Full post...


Angelos Filos, Sebastian Farquhar, Aidan Gomez, Tim G. J. Rudner, Zac Kenton, Lewis Smith, Milad Alizadeh, Yarin Gal, 14 Jun 2019

Are you looking to do a PhD in machine learning? Did you do a PhD in another field and want to do a postdoc in machine learning? Would you like to visit the group?

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Contact

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