Philip J. Ball

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Jack Parker-HolderGoogle DeepMind, UCLVerified email at google.com
Stephen RobertsProfessor of Engineering Science (Machine Learning, Information Engineering), University of OxfordVerified email at robots.ox.ac.uk
Cong LuUniversity of British ColumbiaVerified email at cs.ubc.ca
Karl FristonUniversity College LondonVerified email at ucl.ac.uk
Aldo PacchianoBroad Institute of MIT and HarvardVerified email at broadinstitute.org
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Ilya KostrikovOpenAIVerified email at openai.com
Krzysztof ChoromanskiGoogle DeepMind Robotics & Columbia UniversityVerified email at columbia.edu
Sergey LevineUC Berkeley, Physical IntelligenceVerified email at eecs.berkeley.edu
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Samuel KesslerUniversity of OxfordVerified email at robots.ox.ac.uk
Laura SmithUC BerkeleyVerified email at berkeley.edu
Adrian WellerDirector of Research, Machine Learning, University of CambridgeVerified email at eng.cam.ac.uk
Tim G. J. RudnerFaculty Fellow, New York UniversityVerified email at nyu.edu
Xingchen WanGoogleVerified email at google.com
Tim RocktäschelProfessor of AI at UCL, Open-Endedness Team Lead at Google DeepMind, Fellow ELLISVerified email at cs.ucl.ac.uk
Oleh RybkinPostdoc, University of California, BerkeleyVerified email at seas.upenn.edu

Philip J. Ball

Google DeepMind

Verified email at google.com - Homepage

machine learning reinforcement learning data efficiency diffusion models


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Active inference: demystified and compared N Sajid, PJ Ball, T Parr, KJ Friston Neural Computation 33 (3), 2021	167	2021
Efficient Online Reinforcement Learning with Offline Data PJ Ball, L Smith, I Kostrikov*, S Levine ICML 2023, 2023	99	2023
The sensitivity of counterfactual fairness to unmeasured confounding N Kilbertus, PJ Ball, MJ Kusner, A Weller, R Silva UAI 2019, 2019	64	2019
Revisiting Design Choices in Model-Based Offline Reinforcement Learning C Lu, PJ Ball, J Parker-Holder, MA Osborne, SJ Roberts ICLR 2022 (Spotlight), 2022	59*	2022
Ready policy one: World building through active learning P Ball, J Parker-Holder, A Pacchiano, K Choromanski, S Roberts ICML 2020, 2020	55	2020
Same State, Different Task: Continual Reinforcement Learning without Interference S Kessler, J Parker-Holder, P Ball, S Zohren, SJ Roberts AAAI-22, 2022	44	2022
Towards Tractable Optimism in Model-Based Reinforcement Learning A Pacchiano, P Ball, J Parker-Holder*, K Choromanski, S Roberts UAI 2021, 2021	40*	2021
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations C Lu, PJ Ball, TGJ Rudner, J Parker-Holder, MA Osborne, YW Teh TMLR, 2023	38	2023
Synthetic Experience Replay C Lu, PJ Ball, J Parker-Holder NeurIPS 2023, 2023	37	2023
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment PJ Ball, C Lu, J Parker-Holder, S Roberts ICML 2021, 2021	34	2021
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels E Cetin, PJ Ball, S Roberts, O Celiktutan ICML 2022, 2022	32	2022
Bayesian Generational Population-Based Training X Wan, C Lu, J Parker-Holder, PJ Ball, V Nguyen, B Ru, M Osborne First Conference on Automated Machine Learning (Main Track), 2022	21	2022
Learning General World Models in a Handful of Reward-Free Deployments Y Xu, J Parker-Holder, A Pacchiano, PJ Ball, O Rybkin, SJ Roberts, ... NeurIPS 2022, 2022	8	2022
UNCLEAR: A Straightforward Method for Continual Reinforcement Learning S Kessler, J Parker-Holder, P Ball, S Zohren, SJ Roberts ICML 2020 Workshop on Continual Learning, 2020	7	2020
OffCon: What is state of the art anyway? PJ Ball, SJ Roberts arXiv preprint arXiv:2101.11331, 2021	5	2021
A Study on Efficiency in Continual Learning Inspired by Human Learning PJ Ball, Y Li, A Lamb, C Zhang NeurIPS 2020 BabyMind Workshop, 2020	2	2020
Fairness in Machine Learning with Causal Reasoning P Ball MPhil Thesis, 2018	1	2018
D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning R Rafailov, K Hatch, A Singh, L Smith, A Kumar, I Kostrikov, ... arXiv preprint arXiv:2408.08441, 2024		2024
Effective offline training and efficient online adaptation P Ball University of Oxford, 2023		2023

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