Philip J. Ball

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Jack Parker-HolderResearch Scientist at DeepMindVerified email at google.com
Stephen RobertsProfessor of Engineering Science (Machine Learning, Information Engineering), University of OxfordVerified email at robots.ox.ac.uk
Karl FristonUniversity College LondonVerified email at ucl.ac.uk
Cong LuUniversity of OxfordVerified email at stats.ox.ac.uk
Aldo PacchianoMicrosoft ResearchVerified email at microsoft.com
Krzysztof ChoromanskiGoogle Brain Robotics New York & Columbia UniversityVerified email at columbia.edu
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Adrian WellerDirector of Research, Machine Learning, University of CambridgeVerified email at eng.cam.ac.uk
Samuel KesslerUniversity of OxfordVerified email at robots.ox.ac.uk
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Tim G. J. RudnerIncoming Assistant Professor & Faculty Fellow, New York UniversityVerified email at nyu.edu
Ilya KostrikovOpenAIVerified email at openai.com
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
Laura SmithUC BerkeleyVerified email at berkeley.edu
Xingchen WanUniversity of Oxford; GoogleVerified email at google.com

Philip J. Ball

PhD Student, University of Oxford

Verified email at robots.ox.ac.uk - Homepage

machine learning reinforcement learning data efficiency


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	105*	2021
The sensitivity of counterfactual fairness to unmeasured confounding N Kilbertus, PJ Ball, MJ Kusner, A Weller, R Silva UAI 2019, 2019	48	2019
Ready policy one: World building through active learning P Ball, J Parker-Holder, A Pacchiano, K Choromanski, S Roberts ICML 2020, 2020	40	2020
Towards Tractable Optimism in Model-Based Reinforcement Learning A Pacchiano, P Ball, J Parker-Holder*, K Choromanski, S Roberts UAI 2021, 2021	28*	2021
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	26	2021
Same State, Different Task: Continual Reinforcement Learning without Interference S Kessler, J Parker-Holder, P Ball, S Zohren, SJ Roberts AAAI-22, 2022	16	2022
Efficient Online Reinforcement Learning with Offline Data PJ Ball, L Smith, I Kostrikov*, S Levine ICML 2023, 2023	13*	2023
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations C Lu, PJ Ball, TGJ Rudner, J Parker-Holder, MA Osborne, YW Teh Workshop on Learning from Diverse, Offline Data @ RSS2022 (Outstanding Paper), 2022	12	2022
Revisiting Design Choices in Model-Based Offline Reinforcement Learning C Lu, PJ Ball, J Parker-Holder, MA Osborne, SJ Roberts ICLR 2022 (Spotlight), 2022	12*	2022
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels E Cetin, PJ Ball, S Roberts, O Celiktutan ICML 2022, 2022	7	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	6	2022
OffCon: What is state of the art anyway? PJ Ball, SJ Roberts arXiv preprint arXiv:2101.11331, 2021	3	2021
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	3	2020
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	2	2022
Synthetic Experience Replay C Lu, PJ Ball, J Parker-Holder arXiv preprint arXiv:2303.06614, 2023	1	2023
A Study on Efficiency in Continual Learning Inspired by Human Learning PJ Ball, Y Li, A Lamb, C Zhang NeurIPS 2020 BabyMind Workshop, 2020	1	2020
Fairness in Machine Learning with Causal Reasoning P Ball MPhil Thesis, 2018		2018

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