Philip J. Ball

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Soavtorji

Jack Parker-HolderGoogle DeepMind, UCLPreverjeni e-poštni naslov na google.com
Stephen RobertsProfessor of Engineering Science (Machine Learning, Information Engineering), University of OxfordPreverjeni e-poštni naslov na robots.ox.ac.uk
Cong LuGoogle DeepMindPreverjeni e-poštni naslov na google.com
Karl FristonUniversity College LondonPreverjeni e-poštni naslov na ucl.ac.uk
Ilya KostrikovOpenAIPreverjeni e-poštni naslov na openai.com
Aldo PacchianoBoston University and Broad Institute of MIT and HarvardPreverjeni e-poštni naslov na bu.edu
Michael A OsborneProfessor of Machine Learning, University of OxfordPreverjeni e-poštni naslov na robots.ox.ac.uk
Krzysztof ChoromanskiGoogle DeepMind Robotics & Columbia UniversityPreverjeni e-poštni naslov na columbia.edu
Sergey LevineUC Berkeley, Physical IntelligencePreverjeni e-poštni naslov na eecs.berkeley.edu
Laura SmithUC BerkeleyPreverjeni e-poštni naslov na berkeley.edu
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindPreverjeni e-poštni naslov na stats.ox.ac.uk
Samuel KesslerUniversity of OxfordPreverjeni e-poštni naslov na robots.ox.ac.uk
Adrian WellerDirector of Research, Machine Learning, University of CambridgePreverjeni e-poštni naslov na eng.cam.ac.uk
Tim G. J. RudnerFaculty Fellow, New York UniversityPreverjeni e-poštni naslov na nyu.edu
Xingchen WanGooglePreverjeni e-poštni naslov na google.com
Tim RocktäschelDirector and Open-Endedness Team Lead at Google DeepMind, Professor of AI at UCL, Fellow ELLISPreverjeni e-poštni naslov na cs.ucl.ac.uk
Oleh RybkinPostdoc, University of California, BerkeleyPreverjeni e-poštni naslov na eecs.berkeley.edu

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Philip J. Ball

Google DeepMind

Preverjeni e-poštni naslov na google.com - Domača stran

machine learning reinforcement learning data efficiency diffusion models


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
Active inference: demystified and compared N Sajid, PJ Ball, T Parr, KJ Friston Neural Computation 33 (3), 2021	200	2021
Efficient Online Reinforcement Learning with Offline Data PJ Ball, L Smith, I Kostrikov*, S Levine ICML 2023, 2023	172	2023
Synthetic Experience Replay C Lu, PJ Ball, J Parker-Holder NeurIPS 2023, 2023	76	2023
Revisiting Design Choices in Model-Based Offline Reinforcement Learning C Lu, PJ Ball, J Parker-Holder, MA Osborne, SJ Roberts ICLR 2022 (Spotlight), 2022	76*	2022
The sensitivity of counterfactual fairness to unmeasured confounding N Kilbertus, PJ Ball, MJ Kusner, A Weller, R Silva UAI 2019, 2019	73	2019
Ready policy one: World building through active learning P Ball, J Parker-Holder, A Pacchiano, K Choromanski, S Roberts ICML 2020, 2020	57	2020
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	54	2023
Same State, Different Task: Continual Reinforcement Learning without Interference S Kessler, J Parker-Holder, P Ball, S Zohren, SJ Roberts AAAI-22, 2022	48	2022
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	48	2021
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels E Cetin, PJ Ball, S Roberts, O Celiktutan ICML 2022, 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	41*	2021
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	22	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	11	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	8	2020
OffCon: What is state of the art anyway? PJ Ball, SJ Roberts arXiv preprint arXiv:2101.11331, 2021	7	2021
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	6	2024
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
Genie 2: A Large-Scale Foundation World Model https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world …, 2024		2024
Effective offline training and efficient online adaptation P Ball University of Oxford, 2023		2023

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