Parker-Holder, Jack

57 publications

ICLR 2025 BALROG: Benchmarking Agentic LLM and VLM Reasoning on Games Davide Paglieri, Bartłomiej Cupiał, Samuel Coward, Ulyana Piterbarg, Maciej Wolczyk, Akbir Khan, Eduardo Pignatelli, Łukasz Kuciński, Lerrel Pinto, Rob Fergus, Jakob Nicolaus Foerster, Jack Parker-Holder, Tim Rocktäschel

NeurIPS 2025 Imagined Autocurricula Ahmet H. Güzel, Matthew Thomas Jackson, Jarek Luca Liesen, Tim Rocktäschel, Jakob Nicolaus Foerster, Ilija Bogunovic, Jack Parker-Holder

TMLR 2025 Synthetic Data Is Sufficient for Zero-Shot Visual Generalization from Offline Data Ahmet H. Güzel, Ilija Bogunovic, Jack Parker-Holder

ICML 2024 Genie: Generative Interactive Environments Jake Bruce, Michael D Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Maria Elisabeth Bechtle, Feryal Behbahani, Stephanie C.Y. Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando De Freitas, Satinder Singh, Tim Rocktäschel

ICMLW 2024 Higher Order and Self-Referential Evolution for Population-Based Methods Samuel Coward, Chris Lu, Alistair Letcher, Minqi Jiang, Jack Parker-Holder, Jakob Nicolaus Foerster

ICMLW 2024 Outliers and Calibration Sets Have Diminishing Effect on Quantization of Modern LLMs Davide Paglieri, Saurabh Dash, Tim Rocktäschel, Jack Parker-Holder

ICML 2024 Position: Open-Endedness Is Essential for Artificial Superhuman Intelligence Edward Hughes, Michael D Dennis, Jack Parker-Holder, Feryal Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel

ICML 2024 Position: Video as the New Language for Real-World Decision Making Sherry Yang, Jacob C Walker, Jack Parker-Holder, Yilun Du, Jake Bruce, Andre Barreto, Pieter Abbeel, Dale Schuurmans

NeurIPS 2024 Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H. Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, Tim Rocktäschel, Roberta Raileanu

ICLRW 2024 Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H. Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob Nicolaus Foerster, Tim Rocktäschel, Roberta Raileanu

TMLR 2023 Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A Osborne, Yee Whye Teh

NeurIPS 2023 Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design Matthew T Jackson, Minqi Jiang, Jack Parker-Holder, Risto Vuorio, Chris Lu, Greg Farquhar, Shimon Whiteson, Jakob Foerster

ICML 2023 Human-Timescale Adaptation in an Open-Ended Task Space Jakob Bauer, Kate Baumli, 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, Satinder Singh, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei M Zhang

ICLR 2023 MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning Mikayel Samvelyan, Akbir Khan, Michael D Dennis, Minqi Jiang, Jack Parker-Holder, Jakob Nicolaus Foerster, Roberta Raileanu, Tim Rocktäschel

NeurIPSW 2023 Multi-Agent Diagnostics for Robustness via Illuminated Diversity Mikayel Samvelyan, Davide Paglieri, Minqi Jiang, Jack Parker-Holder, Tim Rocktäschel

CoLLAs 2023 Stabilizing Unsupervised Environment Design with a Learned Adversary Ishita Mediratta, Minqi Jiang, Jack Parker-Holder, Michael Dennis, Eugene Vinitsky, Tim Rocktäschel

NeurIPS 2023 Synthetic Experience Replay Cong Lu, Philip Ball, Yee Whye Teh, Jack Parker-Holder

ICLRW 2023 Synthetic Experience Replay Cong Lu, Philip J. Ball, Jack Parker-Holder

ICMLW 2023 Synthetic Experience Replay Cong Lu, Philip J. Ball, Yee Whye Teh, Jack Parker-Holder

CoLLAs 2023 The Effectiveness of World Models for Continual Reinforcement Learning Samuel Kessler, Mateusz Ostaszewski, MichałPaweł Bortkiewicz, Mateusz Żarski, Maciej Wolczyk, Jack Parker-Holder, Stephen J. Roberts, Piotr Miłoś

NeurIPSW 2023 Vision-Language Models as a Source of Rewards Kate Baumli, Satinder Singh, Feryal Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei M Zhang

AISTATS 2022 Towards an Understanding of Default Policies in Multitask Policy Optimization Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano

JAIR 2022 Automated Reinforcement Learning (AutoRL): A Survey and Open Problems Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer

AutoML 2022 Bayesian Generational Population-Based Training Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael Osborne

ICLRW 2022 Bayesian Generational Population-Based Training Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael Osborne

ICMLW 2022 Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A Osborne, Yee Whye Teh

ICML 2022 Evolving Curricula with Regret-Based Environment Design Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel

ICML 2022 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

NeurIPS 2022 Grounding Aleatoric Uncertainty for Unsupervised Environment Design Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob Foerster

CoLLAs 2022 Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning Michael Matthews, Mikayel Samvelyan, Jack Parker-holder, Edward Grefenstette, Tim Rocktäschel

NeurIPS 2022 Learning General World Models in a Handful of Reward-Free Deployments Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip Ball, Oleh Rybkin, S Roberts, Tim Rocktäschel, Edward Grefenstette

NeurIPSW 2022 MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning Mikayel Samvelyan, Akbir Khan, Michael D Dennis, Minqi Jiang, Jack Parker-Holder, Jakob Nicolaus Foerster, Roberta Raileanu, Tim Rocktäschel

ICLR 2022 Revisiting Design Choices in Offline Model Based Reinforcement Learning Cong Lu, Philip Ball, Jack Parker-Holder, Michael Osborne, Stephen J. Roberts

AAAI 2022 Same State, Different Task: Continual Reinforcement Learning Without Interference Samuel Kessler, Jack Parker-Holder, Philip J. Ball, Stefan Zohren, Stephen J. Roberts

ICLRW 2022 SkillHack: A Benchmark for Skill Transfer in Open-Ended Reinforcement Learning Michael Matthews, Mikayel Samvelyan, Jack Parker-Holder, Edward Grefenstette, Tim Rocktäschel

NeurIPSW 2022 The Surprising Effectiveness of Latent World Models for Continual Reinforcement Learning Samuel Kessler, Piotr Miłoś, Jack Parker-Holder, Stephen J. Roberts

ICML 2021 Augmented World Models Facilitate Zero-Shot Dynamics Generalization from a Single Offline Environment Philip J Ball, Cong Lu, Jack Parker-Holder, Stephen Roberts

ICLRW 2021 Augmented World Models Facilitate Zero-Shot Dynamics Generalization from a Single Offline Environment Philip Ball, Cong Lu, Jack Parker-Holder, S Roberts

NeurIPSW 2021 Grounding Aleatoric Uncertainty in Unsupervised Environment Design Minqi Jiang, Michael D Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Kuttler, Edward Grefenstette, Tim Rocktäschel, Jakob Nicolaus Foerster

NeurIPS 2021 Replay-Guided Adversarial Environment Design Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel

NeurIPSW 2021 Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay Iryna Korshunova, Minqi Jiang, Jack Parker-Holder, Tim Rocktäschel, Edward Grefenstette

NeurIPS 2021 Tactical Optimism and Pessimism for Deep Reinforcement Learning Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan

NeurIPSW 2021 That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities Jack Parker-Holder, Minqi Jiang, Michael D Dennis, Mikayel Samvelyan, Jakob Nicolaus Foerster, Edward Grefenstette, Tim Rocktäschel

UAI 2021 Towards Tractable Optimism in Model-Based Reinforcement Learning Aldo Pacchiano, Philip Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts

NeurIPS 2021 Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL Jack Parker-Holder, Vu Nguyen, Shaan Desai, Stephen J Roberts

NeurIPS 2020 Effective Diversity in Population Based Reinforcement Learning Jack Parker-Holder, Aldo Pacchiano, Krzysztof M Choromanski, Stephen J. Roberts

ICMLW 2020 Effective Diversity in Population Based Reinforcement Learning Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts

ICML 2020 Learning to Score Behaviors for Guided Policy Optimization Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael Jordan

AISTATS 2020 Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang

NeurIPS 2020 Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits Jack Parker-Holder, Vu Nguyen, Stephen J. Roberts

ICML 2020 Ready Policy One: World Building Through Active Learning Philip Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts

NeurIPS 2020 Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob Foerster

ICML 2020 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

ICMLW 2020 Taming the Herd: Multi-Modal Meta-Learning with a Population of Agents Robert Müller, Jack Parker-Holder, Aldo Pacchiano

NeurIPS 2019 From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization Krzysztof M Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani

CoRL 2019 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