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Panagiotis Tigas

PhD (2018—2023)

Panos was a DPhil student in the OATML group in the Department of Computer Science at the University of Oxford, supervised by Yarin Gal. He is interested in representation learning for control and communication, in single and multi-agent systems. Prior to his DPhil he worked as software engineer for Microsoft (Relevance/NLP), Autodesk (AI/Generative Design), Brave (Privacy Preserving ML) and his own startup Filisia (Special Education Tech). He has an MSc in Machine Learning from University of Bristol. In his spare time, he enjoys making music and interactive art.

Publications while at OATMLNews items mentioning Panagiotis TigasReproducibility and CodeBlog Posts

Publications while at OATML:

Differentiable Multi-Target Causal Bayesian Experimental Design

We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting --- a critical component for causal discovery from finite data where interventions can be costly or risky. Existing methods rely on greedy approximations to construct a batch of experiments while using black-box methods to optimize over a single target-state pair to intervene with. In this work, we completely dispose of the black-box optimization techniques and greedy heuristics and instead propose a conceptually simple end-to-end gradient-based optimization procedure to acquire a set of optimal intervention target-value pairs. Such a procedure enables parameterization of the design space to efficiently optimize over a batch of multi-target-state interventions, a setting which has hitherto not been explored due to its complexity. We demonstrate that our proposed method outperforms baselines and existing acquisition strategies in both single-target... [full abstract]

Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
ICML, 2023
Machine Learning for Drug Discovery Workshop (spotlight), ICLR 2023
Differentiable Multi-Target Causal Bayesian Experimental Design, ICML 2023
[arXiv] [BibTex]

Modelling non-reinforced preferences using selective attention

How can artificial agents learn non-reinforced preferences to continuously adapt their behaviour to a changing environment? We decompose this question into two challenges: (i) encoding diverse memories and (ii) selectively attending to these for preference formation. Our proposed non-reinforced preference learning mechanism using selective attention, Nore, addresses both by leveraging the agent's world model to collect a diverse set of experiences which are interleaved with imagined roll-outs to encode memories. These memories are selectively attended to, using attention and gating blocks, to update agent's preferences. We validate Nore in a modified OpenAI Gym FrozenLake environment (without any external signal) with and without volatility under a fixed model of the environment -- and compare its behaviour to Pepper, a Hebbian preference learning mechanism. We demonstrate that Nore provides a straightforward framework to induce exploratory preferences in the absence of external si... [full abstract]

Noor Sajid, Panagiotis Tigas, Zafeirios Fountas, Qinghai Guo, Alexey Zakharov, Lancelot Da Costa
Workshop Track - 1st Conference on Lifelong Learning Agents, 2022

Interventions, Where and How? Experimental Design for Causal Models at Scale

Causal discovery from observational and interventional data is challenging due to limited data and non-identifiability: factors that introduce uncertainty in estimating the underlying structural causal model (SCM). Selecting experiments (interventions) based on the uncertainty arising from both factors can expedite the identification of the SCM. Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target. This work incorporates recent advances in Bayesian causal discovery into the Bayesian optimal experimental design framework, allowing for active causal discovery of large, nonlinear SCMs while selecting both the interventional target and the value. We demonstrate the performance of the proposed method on synthetic graphs (Erdos-Rènyi, Scale Free) for both linear and nonlinear SCMs as well as on the in-silico single-cell gene regulatory network dataset, DREAM.

Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
NeurIPS, 2022
Adaptive Experimental Design and Active Learning in the Real World, NeurIPS 2022
[arXiv] [BibTex]

Global Geomagnetic Perturbation Forecasting Using Deep Learning

Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field, which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time variability of the horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs. In this work, we develop a fast, global dB/dt forecasting model, which forecasts 30 min into the future using only solar wind measurements as input. The model summarizes 2 hr of solar wind measurement using a Gated Recurrent Unit and generates forecasts of coefficients that are folded with a spherical harmonic basis to enable global forecasts. When deployed, our model produces results i... [full abstract]

Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi, Téo Bloch, Mark C. M. Cheung, Siddha Ganju, Asti Bhatt, Ryan M. McGranaghan, Yarin Gal
Space Weather

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects

Estimating personalized treatment effects from high-dimensional observational data is essential in situations where experimental designs are infeasible, unethical or expensive. Existing approaches rely on fitting deep models on outcomes observed for treated and control populations, but when measuring the outcome for an individual is costly (e.g. biopsy) a sample efficient strategy for acquiring outcomes is required. Deep Bayesian active learning provides a framework for efficient data acquisition by selecting points with high uncertainty. However, naive application of existing methods selects training data that is biased toward regions where the treatment effect cannot be identified because there is non-overlapping support between the treated and control populations. To maximize sample efficiency for learning personalized treatment effects, we introduce new acquisition functions grounded in information theory that bias data acquisition towards regions where overlap is satisfied, by... [full abstract]

Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
NeurIPS, 2021

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks

There has been significant research done on developing methods for improving robustness to distributional shift and uncertainty estimation. In contrast, only limited work has examined developing standard datasets and benchmarks for assessing these approaches. Additionally, most work on uncertainty estimation and robustness has developed new techniques based on small-scale regression or image classification tasks. However, many tasks of practical interest have different modalities, such as tabular data, audio, text, or sensor data, which offer significant challenges involving regression and discrete or continuous structured prediction. Thus, given the current state of the field, a standardized large-scale dataset of tasks across a range of modalities affected by distributional shifts is necessary. This will enable researchers to meaningfully evaluate the plethora of recently developed uncertainty quantification methods, as well as assessment criteria and state-of-the-art baselines.... [full abstract]

Andrey Malinin, Neil Band, Alexander Ganshin, German Chesnokov, Yarin Gal, Mark J. F. Gales, Alexey Noskov, Andrey Ploskonosov, Liudmila Prokhorenkova, Ivan Provilkov, Vatsal Raina, Vyas Raina, Denis Roginskiy, Mariya Shmatova, Panagiotis Tigas, Boris Yangel
NeurIPS Datasets and Benchmarks Track, 2021
[arXiv] [BibTex] [Code]
[Competition Website] [Blog Post (OATML)] [Blog Post (Yandex Research)]

Emergent Interfaces; Vague, Complex, Bespoke and Embodied Interaction between Humans and Computers

Most Human–Computer Interfaces are built on the paradigm of manipulating abstract representations. This can be limiting when computers are used in artistic performance or as mediators of social connection, where we rely on qualities of embodied thinking: intuition, context, resonance, ambiguity and fluidity. We explore an alternative approach to designing interaction that we call the emergent interface: interaction leveraging unsupervised machine learning to replace designed abstractions with contextually derived emergent representations. The approach offers opportunities to create interfaces bespoke to a single individual, to continually evolve and adapt the interface in line with that individual’s needs and affordances, and to bridge more deeply with the complex and imprecise interaction that defines much of our non-digital communication. We explore this approach through artistic research rooted in music, dance and AI with the partially emergent system Sonified Body. The system m... [full abstract]

Tim Murray-Browne, Panagiotis Tigas
Applied Sciences 11(18)

Exploration and preference satisfaction trade-off in reward-free learning

Biological agents have meaningful interactions with their environment despite the absence of immediate reward signals. In such instances, the agent can learn preferred modes of behaviour that lead to predictable states -- necessary for survival. In this paper, we pursue the notion that this learnt behaviour can be a consequence of reward-free preference learning that ensures an appropriate trade-off between exploration and preference satisfaction. For this, we introduce a model-based Bayesian agent equipped with a preference learning mechanism (pepper) using conjugate priors. These conjugate priors are used to augment the expected free energy planner for learning preferences over states (or outcomes) across time. Importantly, our approach enables the agent to learn preferences that encourage adaptive behaviour at test time. We illustrate this in the OpenAI Gym FrozenLake and the 3D mini-world environments -- with and without volatility. Given a constant environment, these agents le... [full abstract]

Noor Sajid, Panagiotis Tigas, Alexey Zakharov, Zafeirios Fountas, Karl Friston
ICML 2021 Workshop on Unsupervised Reinforcement Learning

Latent Mappings; Generating Open-Ended Expressive Mappings Using Variational Autoencoders

In many contexts, creating mappings for gestural interactions can form part of an artistic process. Creators seeking a mapping that is expressive, novel, and affords them a sense of authorship may not know how to program it up in a signal processing patch. Tools like Wekinator and MIMIC allow creators to use supervised machine learning to learn mappings from example input/output pairings. However, a creator may know a good mapping when they encounter it yet start with little sense of what the inputs or outputs should be. We call this an open-ended mapping process. Addressing this need, we introduce the latent mapping, which leverages the latent space of an unsupervised machine learning algorithm such as a Variational Autoencoder trained on a corpus of unlabelled gestural data from the creator. We illustrate it with Sonified Body, a system mapping full-body movement to sound which we explore in a residency with three dancers.

Tim Murray-Browne, Panagiotis Tigas

Global Earth Magnetic Field Modeling andForecasting with Spherical Harmonics Decomposition

Modeling and forecasting the solar wind-driven global magnetic field perturbations is an open challenge. Current approaches depend on simulations of computationally demanding models like the Magnetohydrodynamics (MHD) model or sampling spatially and temporally through sparse ground-based stations (SuperMAG). In this paper, we develop a Deep Learning model that forecasts in Spherical Harmonics space, replacing reliance on MHD models and providing global coverage at oneminute cadence, improving over the current state-of-the-art which relies on feature engineering. We evaluate the performance in SuperMAG dataset (improved by 14.53%) and MHD simulations (improved by 24.35%). Additionally, we evaluate the extrapolation performance of the spherical harmonics reconstruction based on sparse ground-based stations (SuperMAG), showing that spherical harmonics can reliably reconstruct the global magnetic field as evaluated on MHD simulation

Panagiotis Tigas, Téo Bloch, Vishal Upendran, Banafsheh Ferdoushi, Yarin Gal, Siddha Ganju, Ryan M. McGranaghan, Mark C. M. Cheung, Asti Bhatt
Machine Learning and the Physical Sciences Workshop - 34th NeurIPS 2020 [Paper]
Determining new representations of “Geoeffectiveness” using deep learning - AGU 2020

Real2sim: Automatic Generation of Open Street Map Towns For Autonomous Driving Benchmarks

Research in machine learning for autonomous driving (AD) is a constantly evolving field as researchers strive to build a Level 5 autonomous driving system. However, current benchmarks for such learning algorithms do not satisfactorily allow researchers to evaluate and compare performance across safety-critical metrics such as generalizability, out-of-distribution performance, etc. Reasons for this include the expensive nature of data collection from the real-world for autonomous driving and the limitations of software tools currently available for autonomous driving simulators. We develop a pipeline that allows for automatic generation of new town maps for simulator environments from OpenStreetMap [Haklay and Weber, 2008]. We demonstrate that our pipeline is capable of generating towns that, when perceived via LiDAR , share similar footprint to real-world gathered datasets like NuScenes [Caesar et al., 2020]. Additionally, we learn a realistic noise augmentation via Conditional Adv... [full abstract]

Avishek Mondal, Panagiotis Tigas, Yarin Gal
Machine Learning for Autonomous Driving Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. [Paper]

Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search

With this work we investigate the use of Reinforcement Learning (RL) for generation of spatial assemblies, by combining ideas from Procedural Generation algorithms (Wave Function Collapse algorithm (WFC)) and RL for Game Solving. WFC is a Generative Design algorithm, inspired by Constraint Satisfaction Solvers. In WFC,one defines a set of tiles/blocks and constraints and the algorithm generates an assembly that satisfies these constraints. Casting the problem of generation of spatial assemblies as a Markov Decision Process whose states transitions are defined by WFC, we propose an algorithm that uses Reinforcement Learning and Self-Play to learn a policy that generates assemblies which maximize objectives set by the designer. We demonstrate the use of our Spatial Assembly algorithm in Architecture Design.

Panagiotis Tigas, Tyson Hosmer
Workshop on Machine Learning for Creativity and Design at the 34rd Conference on Neural Information Processing Systems (NeurIPS 2020) [Paper]

Percival; Making In-Browser Perceptual Ad Blocking Practical with Deep Learning

In this paper we present PERCIVAL, a browser-embedded, lightweight, deep learning-powered ad blocker. PERCIVAL embeds itself within the browser’s image rendering pipeline, which makes it possible to intercept every image obtained during page execution and to perform image classification based blocking to flag potential ads. Our implementation inside both Chromium and Brave browsers shows only a minor rendering performance overhead of 4.55%, for Chromium, and 19.07%, for Brave browser, demonstrating the feasibility of deploying traditionally heavy models (ie deep neural networks) inside the critical path of the rendering engine of a browser. We show that our image-based ad blocker can replicate EasyList rules with an accuracy of 96.76%. Additionally, PERCIVAL does surprisingly well on ads in languages other than English and also performs well on blocking first-party Facebook ads, which have presented issues for rule-based ad blockers. PERCIVAL proves that image-based perceptual ad b... [full abstract]

Zainul Abi Din, Panagiotis Tigas, Samuel T King, Benjamin Livshits
'2020 USENIX Annual Technical Conference

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions. In principle, detection of and adaptation to OOD scenes can mitigate their adverse effects. In this paper, we highlight the limitations of current approaches to novel driving scenes and propose an epistemic uncertainty-aware planning method, called _robust imitative planning_ (RIP). Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. If the model's uncertainty is too great to suggest a safe course of action, the model can instead query the expert driver for feedback, enabling sample-efficient online adaptation, a variant of our method we term _adaptive robust imitative planning_ (AdaRIP). Our methods outperform current state-of-the-art approaches in the nuScenes _prediction_ challenge, but since no benchmark evaluating OOD d... [full abstract]

Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal
ICML, 2020
[Paper] [Code] [Website]

Robust Imitative Planning: Planning from Demonstrations Under Uncertainty

Learning from expert demonstrations is an attractive framework for sequential decision-making in safety-critical domains such as autonomous driving, where trial and error learning has no safety guarantees during training. However, naïve use of imitation learning can fail by extrapolating incorrectly to unfamiliar situations, resulting in arbitrary model outputs and dangerous outcomes. This is especially true for high capacity parametric models such as deep neural networks, for processing high-dimensional observations from cameras or LIDAR. Instead, we model expert behaviour with a model able to capture uncertainty about previously unseen scenarios, as well as inherent stochasticity in expert demonstrations. We propose a framework for planning under epistemic uncertainty and also provide a practical realisation, called robust imitative planning (RIP), using an ensemble of deep neural density estimators. We demonstrate online robustness to out-of-training distribution scenarios on th... [full abstract]

Panagiotis Tigas, Angelos Filos, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal
NeurIPS2019 Workshop on Machine Learning for Autonomous Driving
More publications on Google Scholar.

News items mentioning Panagiotis Tigas:

DAGGER featured in NASA website

DAGGER featured in NASA website

30 Mar 2023

DPhil student Panagiotis Tigas and his collaborators from Frontiers Development Lab published their work on predicting geomagnetic pertubations using deep learning (DAGGER) in the Space Weather journal. The work has been featured in NASA’s main website

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Panagiotis Tigas to present at Wellcome Centre of Human Neuroimaging, UCL

Panagiotis Tigas to present at Wellcome Centre of Human Neuroimaging, UCL

15 Mar 2022

OATML DPhil student Panagiotis Tigkas will give an invited talk at Wellcome Centre of Human Neuroimaging, UCL on Bayesian Optimal Experimental Design for Causal Discovery.

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Panagiotis Tigas to present at Machine Learning for Heliophysics conference

Panagiotis Tigas to present at Machine Learning for Heliophysics conference

14 Mar 2022

OATML DPhil student Panagiotis Tigkas will give a talk at Machine Learning for Heliophysics conference on global geomagnetic perturbation forecasting using deep learning.

Link to this news item
NeurIPS 2021

NeurIPS 2021

11 Oct 2021

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

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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.

Link to this news item
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.

Link to this news item

Reproducibility and Code

OATomobile: A research framework for autonomous driving

OATomobile is a library for autonomous driving research. OATomobile strives to expose simple, efficient, well-tuned and readable agents, that serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research.

Angelos Filos, Panagiotis Tigas

Blog Posts

OATML Conference papers at NeurIPS 2022

OATML group members and collaborators are proud to present 8 papers at NeurIPS 2022 main conference, and 11 workshop papers. …

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Yarin Gal, Freddie Kalaitzis, Shreshth Malik, Lorenz Kuhn, Gunshi Gupta, Jannik Kossen, Pascal Notin, Andrew Jesson, Panagiotis Tigas, Tim G. J. Rudner, Sebastian Farquhar, Ilia Shumailov, 25 Nov 2022

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

Introducing the Shifts Challenge

We have released the Shifts benchmark for robustness and uncertainty quantification, along with our accompanying NeurIPS 2021 Challenge! We believe that Shifts, which includes the largest vehicle motion prediction dataset to date, will become the standard large-scale evaluation suite for uncertainty and robustness in machine learning. …

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Neil Band, Andrey Malinin, Panagiotis Tigas, Yarin Gal, 06 Aug 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

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

In autonomous driving, we generally train models on diverse data to maximize the coverage of possible situations the vehicle may encounter at deployment. Global data coverage would be ideal, but impossible to collect, necessitating methods that can generalize safely to new scenarios. As human drivers, we do not need to re-learn how to drive in every city, even though every city is unique. Hence, we’d like a system trained in Pittsburgh and Los Angeles to also be safe when deployed in New York, where the landscape and behaviours of the drivers is different. …

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Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal, 09 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. …

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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

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