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Human-Timescale Adaptation in an Open-Ended Task Space
Adaptive Agent Team, Jakob Bauer, Kate Baumli, Satinder Baveja, Feryal Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez-Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei Zhang
The International Conference on Machine Learnning (ICML), 2023 (Oral)
(Paper) (Website) (New Scientist)
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Evolving Curricula with Regret-Based Environment Design
Jack Parker-Holder*, Minqi Jiang*, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel
The International Conference on Machine Learning (ICML), 2022
(Paper) (Website) (Paper Review) (Video Interview)
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From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
Krzysztof Choromanski*, Han Lin*, Haoxian Chen*, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamas Sarlos, Adrian Weller, Thomas Weingarten
The International Conference on Machine Learning (ICML), 2022
(ArXiv)
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Revisiting Design Choices in Offline Model Based Reinforcement Learning
Cong Lu*, Philip Ball*, Jack Parker-Holder, Michael Osborne, Stephen Roberts
International Conference on Learning Representations (ICLR), 2022 (Spotlight)
Reinforcement Learning for Real Life Workshop @ ICML, 2021 (Spotlight)
(Paper)
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Towards an Understanding of Default Policies in Multitask Policy Optimization
Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano
Artificial Intelligence and Statistics (AISTATS), 2022 (Oral, Best Paper Honorable Mention)
(Paper)
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On-the-fly Strategy Adaptation for ad-hoc Agent Coordination
Jaleh Zand, Jack Parker-Holder, Stephen Roberts
International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2022
Cooperative AI Workshop, NeurIPS, 2021
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Lyapunov Exponents for Diversity in Differentiable Games
Jonathan Lorraine, Paul Vicol, Jack Parker-Holder, Tal Kachman, Luke Metz, Jakob Foerster
International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2022
Beyond first-order methods in ML systems workshop, ICML, 2021
(Paper)
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Same State, Different Task: Continual Reinforcement Learning without Interference
Samuel Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren, Stephen Roberts
AAAI Conference on Artificial Intelligence, 2022
Workshop on Continual Learning, ICML, 2020
(Paper)
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Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL
Jack Parker-Holder, Vu Nguyen, Shaan Desai, Stephen Roberts
Advances in Neural Information Processing Systems (NeurIPS), 2021
(ArXiv)
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Replay-Guided Adversarial Environment Design
Minqi Jiang*, Michael Dennis*, Jack Parker-Holder, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel
Advances in Neural Information Processing Systems (NeurIPS), 2021
(ArXiv)
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Tactical Optimism and Pessimism for Deep Reinforcement Learning
Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan
Advances in Neural Information Processing Systems (NeurIPS), 2021
(ArXiv)
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MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Kuttler, Edward Grefenstette, Tim Rocktäschel
NeurIPS (Datasets and Benchmarks Track), 2021
(Paper)
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Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment
Philip J Ball*, Cong Lu*, Jack Parker-Holder, Stephen Roberts
The International Conference on Machine Learning (ICML), 2021
Workshop on Self-Supervision in Reinforcement Learning, ICLR, 2021 (Spotlight)
(Paper) (Website)
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Towards Tractable Optimism in Model-Based Reinforcement Learning
Aldo Pacchiano*, Philip Ball*, Jack Parker-Holder*, Krzysztof Choromanski, Stephen Roberts
Uncertainty in Artificial Intelligence (UAI), 2021
(ArXiv)
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Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits
Jack Parker-Holder, Vu Nguyen, Stephen Roberts
Advances in Neural Information Processing Systems (NeurIPS), 2020
The 7th ICML Workshop on Automated Machine Learning (AutoML), 2020 (Contributed Talk)
(ArXiv) (Blog) (Code)
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Ridge Rider: Finding diverse solutions by following eigenvectors of the Hessian
Jack Parker-Holder*, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair HP Letcher, Alex Peysakhovich, Aldo Pacchiano, Jakob Foerster*
Advances in Neural Information Processing Systems (NeurIPS), 2020
Beyond First Order Methods in ML Systems, ICML, 2020 (Spotlight)
(Arxiv) (Blog)
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Stochastic Flows and Geometric Optimization on the Orthogonal Group
Krzysztof Choromanski*, David Cheikhi*, Jared Davis*, Valerii Likhosherstov*, Achille Nazaret*, Achraf Bahamou*, Xingyou Song*, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamas Sarlos, Adrian Weller, Vikas Sindhwani
The International Conference on Machine Learning (ICML), 2020
(ArXiv)
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Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes
Krzysztof Choromanski*, Aldo Pacchiano*, Jack Parker-Holder*, Yunhao Tang*
Artificial Intelligence and Statistics (AISTATS), 2020
(ArXiv)
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Provably Robust Blackbox Optimization for Reinforcement Learning
Krzysztof Choromanski*, Aldo Pacchiano*, Jack Parker-Holder*, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani
The Conference on Robot Learning (CoRL), 2019 (Spotlight)
(ArXiv)
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Grounding Aleatoric Uncertainty in Unsupervised Environment Design
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob Foerster
Deep RL Workshop, NeurIPS, 2021
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Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay
Iryna Korshunova, Minqi Jiang, Jack Parker-Holder, Tim Rocktäschel, Edward Grefenstette
Deep RL Workshop, NeurIPS, 2021
I (Still) Can’t Believe It’s Not Better Workshop, NeurIPS, 2021
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Taming the Herd: Multi-Modal Meta-Learning with a Population of Agents
Robert Müller, Jack Parker-Holder, Aldo Pacchiano
The Fourth Lifelong Machine Learning Workshop at ICML, 2020
(Paper)
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Reinforcement Learning with Chromatic Networks for Compact Architecture Search
Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Wenbo Gao, Aldo Pacchiano, Tamas Sarlos, Deepali Jain, Yuxiang Yang
The ICLR Workshop on Neural Architecture Search, 2020
(ArXiv)
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Unlocking Pixels for Reinforcement Learning via Implicit Attention
Krzysztof Choromanski*, Deepali Jain*, Jack Parker-Holder*, Xingyou Song*, Valerii Likhosherstov, Anirban Santara, Aldo Pacchiano, Yunhao Tang, Adrian Weller
(ArXiv)
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Facebook AI Research - Intern, FAIR Labs - (2021)
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Aspect Capital - Intern, Machine Learning Research - (2020)
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JPMorgan Chase - Vice President, Quantitative Research - (2012-2019)
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University of Oxford - DPhil Machine Learning - (2019-)
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Columbia University - MA QMSS - (2016-2018)
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University of Exeter - BSc Mathematics - (2009-2012)
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