Jack Parker-Holder

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Aldo PacchianoBoston University and Broad Institute of MIT and HarvardDirección de correo verificada de bu.edu
Tim RocktäschelDirector and Open-Endedness Team Lead at Google DeepMind, Professor of AI at UCL, Fellow ELLISDirección de correo verificada de cs.ucl.ac.uk
Krzysztof ChoromanskiGoogle DeepMind Robotics & Columbia UniversityDirección de correo verificada de columbia.edu
Stephen RobertsProfessor of Engineering Science (Machine Learning, Information Engineering), University of OxfordDirección de correo verificada de robots.ox.ac.uk
Philip J. BallGoogle DeepMindDirección de correo verificada de google.com
Jakob FoersterAssociate Professor, University of OxfordDirección de correo verificada de eng.ox.ac.uk
Michael DennisGoogle DeepMindDirección de correo verificada de cs.berkeley.edu
Mikayel SamvelyanGoogle DeepMindDirección de correo verificada de google.com
Cong LuUniversity of British ColumbiaDirección de correo verificada de cs.ubc.ca
Jake BruceResearch Scientist, DeepMindDirección de correo verificada de google.com
Yuge (Jimmy) ShiGoogle DeepmindDirección de correo verificada de google.com
Edward HughesStaff Research Engineer, DeepMindDirección de correo verificada de google.com
Jeff CluneProfessor, Comp. Sci.,U British Columbia; CIFAR AI Chair, Vector; Senior Research Advisor, DeepMindDirección de correo verificada de ubc.ca
Ashley EdwardsMember of Technical Staff, RunwayDirección de correo verificada de runwayml.com
Satinder SinghGoogle DeepMind / U. of MichiganDirección de correo verificada de umich.edu
Roberta RaileanuResearch Scientist at Meta, Honorary Lecturer at UCL

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Jack Parker-Holder

Google DeepMind, UCL

Dirección de correo verificada de google.com - Página principal

Open-Endedness Artificial Intelligence World Models


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Effective diversity in population based reinforcement learning J Parker-Holder, A Pacchiano, KM Choromanski, SJ Roberts Advances in Neural Information Processing Systems 33, 18050-18062, 2020	176	2020
Evolving curricula with regret-based environment design J Parker-Holder, M Jiang, M Dennis, M Samvelyan, J Foerster, ... International Conference on Machine Learning, 17473-17498, 2022	128	2022
Genie: Generative Interactive Environments J Bruce, M Dennis, A Edwards, J Parker-Holder, Y Shi, E Hughes, M Lai, ... ICML 2024 (Best Paper Award), 2024	124	2024
Human-timescale adaptation in an open-ended task space AA Team, J Bauer, K Baumli, S Baveja, F Behbahani, A Bhoopchand, ... ICML 2023 (Oral), 2023	118*	2023
Automated reinforcement learning (autorl): A survey and open problems J Parker-Holder, R Rajan, X Song, A Biedenkapp, Y Miao, T Eimer, ... Journal of Artificial Intelligence Research 74, 517-568, 2022	108	2022
Replay-guided adversarial environment design M Jiang, M Dennis, J Parker-Holder, J Foerster, E Grefenstette, ... Advances in Neural Information Processing Systems 34, 1884-1897, 2021	102	2021
Minihack the planet: A sandbox for open-ended reinforcement learning research M Samvelyan, R Kirk, V Kurin, J Parker-Holder, M Jiang, E Hambro, ... arXiv preprint arXiv:2109.13202, 2021	100	2021
Provably efficient online hyperparameter optimization with population-based bandits J Parker-Holder, V Nguyen, SJ Roberts Advances in neural information processing systems 33, 17200-17211, 2020	89	2020
Revisiting Design Choices in Offline Model-Based Reinforcement Learning C Lu, PJ Ball, J Parker-Holder, MA Osborne, SJ Roberts arXiv preprint arXiv:2110.04135, 2021	68*	2021
Tactical optimism and pessimism for deep reinforcement learning T Moskovitz, J Parker-Holder, A Pacchiano, M Arbel, M Jordan Advances in Neural Information Processing Systems 34, 12849-12863, 2021	64	2021
Synthetic Experience Replay C Lu, PJ Ball, YW Teh, J Parker-Holder NeurIPS 2023, 2023	62	2023
Ready policy one: World building through active learning P Ball, J Parker-Holder, A Pacchiano, K Choromanski, S Roberts International Conference on Machine Learning, 591-601, 2020	57	2020
From complexity to simplicity: Adaptive es-active subspaces for blackbox optimization KM Choromanski, A Pacchiano, J Parker-Holder, Y Tang, V Sindhwani Advances in Neural Information Processing Systems 32, 2019	56	2019
Same state, different task: Continual reinforcement learning without interference S Kessler, J Parker-Holder, P Ball, S Zohren, SJ Roberts Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7143-7151, 2022	54*	2022
Challenges and opportunities in offline reinforcement learning from visual observations C Lu, PJ Ball, TGJ Rudner, J Parker-Holder, MA Osborne, YW Teh arXiv preprint arXiv:2206.04779, 2022	46	2022
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts M Samvelyan, SC Raparthy, A Lupu, E Hambro, AH Markosyan, M Bhatt, ... arXiv preprint arXiv:2402.16822, 2024	45*	2024
Learning to score behaviors for guided policy optimization A Pacchiano, J Parker-Holder, Y Tang, K Choromanski, A Choromanska, ... International Conference on Machine Learning, 7445-7454, 2020	45	2020
Augmented world models facilitate zero-shot dynamics generalization from a single offline environment PJ Ball, C Lu, J Parker-Holder, S Roberts International Conference on Machine Learning, 619-629, 2021	44	2021
Provably robust blackbox optimization for reinforcement learning K Choromanski, A Pacchiano, J Parker-Holder, Y Tang, D Jain, Y Yang, ... Conference on Robot Learning, 683-696, 2020	43	2020
Towards tractable optimism in model-based reinforcement learning A Pacchiano, P Ball, J Parker-Holder, K Choromanski, S Roberts Uncertainty in Artificial Intelligence, 1413-1423, 2021	41*	2021

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