Compressing Deep Neural Networks: A New Hashing Pipeline Using Kac's Random Walk Matrices. - Jack Parker-Holder; Sam Gass (CoRR, 2018)

Provably Robust Blackbox Optimization for Reinforcement Learning. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang; Deepali Jain; Yuxiang Yang 007; Atil Iscen; Jasmine Hsu; Vikas Sindhwani (CoRL, 2019)

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

When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Jasmine Hsu; Atil Iscen; Deepali Jain; Vikas Sindhwani (CoRR, 2019)

Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang (CoRR, 2019)

Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang (CoRR, 2019)

Wasserstein Reinforcement Learning. - Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang; Anna Choromanska; Krzysztof Choromanski; Michael I. Jordan (CoRR, 2019)

Reinforcement Learning with Chromatic Networks. - Xingyou Song; Krzysztof Choromanski; Jack Parker-Holder; Yunhao Tang; Wenbo Gao; Aldo Pacchiano; Tamás Sarlós; Deepali Jain; Yuxiang Yang 007 (CoRR, 2019)

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

Ready Policy One: World Building Through Active Learning. - Philip J. Ball; Jack Parker-Holder; Aldo Pacchiano; Krzysztof Choromanski; Stephen J. Roberts (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 038; Aldo Pacchiano; et al. (ICML, 2020)

Learning to Score Behaviors for Guided Policy Optimization. - Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang; Krzysztof Choromanski; Anna Choromanska; Michael I. Jordan (ICML, 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 N. Foerster (NeurIPS, 2020)

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

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

One-Shot Bayes Opt with Probabilistic Population Based Training. - Jack Parker-Holder; Vu Nguyen; Stephen Roberts 001 (CoRR, 2020)

On Optimism in Model-Based Reinforcement Learning. - Aldo Pacchiano; Philip J. Ball; Jack Parker-Holder; Krzysztof Choromanski; Stephen Roberts 001 (CoRR, 2020)

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

Insights From the NeurIPS 2021 NetHack Challenge. - Eric Hambro; Sharada P. Mohanty; Dmitrii Babaev; Minwoo Byeon; Dipam Chakraborty; Edward Grefenstette; Minqi Jiang; DaeJin Jo; Anssi Kanervisto; Jongmin Kim 006; Sungwoong Kim; Robert Kirk; et al. (NeurIPS (Competition and Demos), 2021)

Replay-Guided Adversarial Environment Design. - Minqi Jiang; Michael Dennis 001; Jack Parker-Holder; Jakob N. Foerster; Edward Grefenstette; Tim Rocktäschel (NeurIPS, 2021)

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

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 Küttler; Edward Grefenstette; Tim Rocktäschel (NeurIPS Datasets and Benchmarks, 2021)

Towards tractable optimism in model-based reinforcement learning. - Aldo Pacchiano; Philip J. Ball; Jack Parker-Holder; Krzysztof Choromanski; Stephen Roberts 001 (UAI, 2021)

ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning. - Xingyou Song; Krzysztof Choromanski; Jack Parker-Holder; Yunhao Tang; Daiyi Peng; Deepali Jain; Wenbo Gao; Aldo Pacchiano; Tamás Sarlós; Yuxiang Yang 007 (CoRR, 2021)

Deep Reinforcement Learning with Dynamic Optimism. - Ted Moskovitz; Jack Parker-Holder; Aldo Pacchiano; Michael Arbel (CoRR, 2021)

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 (CoRR, 2021)

Graph Kernel Attention Transformers. - Krzysztof Choromanski; Han Lin; Haoxian Chen 002; Jack Parker-Holder (CoRR, 2021)

Revisiting Design Choices in Model-Based Offline Reinforcement Learning. - Cong Lu; Philip J. Ball; Jack Parker-Holder; Michael A. Osborne; Stephen J. Roberts (CoRR, 2021)

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

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

Lyapunov Exponents for Diversity in Differentiable Games. - Jonathan Lorraine; Paul Vicol; Jack Parker-Holder; Tal Kachman; Luke Metz; Jakob N. Foerster (AAMAS, 2022)

On-the-fly Strategy Adaptation for ad-hoc Agent Coordination. - Jaleh Zand; Jack Parker-Holder; Stephen J. Roberts (AAMAS, 2022)

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

Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning. - Michael T. Matthews; Mikayel Samvelyan; Jack Parker-Holder; Edward Grefenstette; Tim Rocktäschel (CoLLAs, 2022)

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

From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers. - Krzysztof Choromanski; Han Lin; Haoxian Chen 002; Tianyi Zhang; Arijit Sehanobish; Valerii Likhosherstov; Jack Parker-Holder; Tamás Sarlós; Adrian Weller; Thomas Weingarten (ICML, 2022)

Evolving Curricula with Regret-Based Environment Design. - Jack Parker-Holder; Minqi Jiang; Michael Dennis 001; Mikayel Samvelyan; Jakob N. Foerster; Edward Grefenstette; Tim Rocktäschel (ICML, 2022)

Grounding Aleatoric Uncertainty for Unsupervised Environment Design. - Minqi Jiang; Michael Dennis 001; Jack Parker-Holder; Andrei Lupu; Heinrich Küttler; Edward Grefenstette; Tim Rocktäschel; Jakob N. Foerster (NeurIPS, 2022)

Learning General World Models in a Handful of Reward-Free Deployments. - Yingchen Xu; Jack Parker-Holder; Aldo Pacchiano; Philip J. Ball; Oleh Rybkin; Stephen Roberts 001; Tim Rocktäschel; Edward Grefenstette (NeurIPS, 2022)

The Surprising Effectiveness of Latent World Models for Continual Reinforcement Learning. - Samuel Kessler; Piotr Milos; Jack Parker-Holder; Stephen J. Roberts (CoRR, 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 (J. Artif. Intell. Res., 2022)

The Effectiveness of World Models for Continual Reinforcement Learning. - Samuel Kessler; Mateusz Ostaszewski; Michal Pawel Bortkiewicz; Mateusz Zarski; Maciej Wolczyk; Jack Parker-Holder; Stephen J. Roberts; Piotr Milos (CoLLAs, 2023)

Stabilizing Unsupervised Environment Design with a Learned Adversary. - Ishita Mediratta; Minqi Jiang; Jack Parker-Holder; Michael Dennis 001; Eugene Vinitsky; Tim Rocktäschel (CoLLAs, 2023)

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning. - Mikayel Samvelyan; Akbir Khan; Michael Dennis 001; Minqi Jiang; Jack Parker-Holder; Jakob Nicolaus Foerster; Roberta Raileanu; Tim Rocktäschel (ICLR, 2023)

Human-Timescale Adaptation in an Open-Ended Task Space. - Jakob Bauer; Kate Baumli; Feryal M. P. Behbahani; Avishkar Bhoopchand; Nathalie Bradley-Schmieg; Michael Chang; Natalie Clay; Adrian Collister; Vibhavari Dasagi; Lucy Gonzalez; Karol Gregor; Edward Hughes 001; et al. (ICML, 2023)

Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design. - Matthew Thomas Jackson; Minqi Jiang; Jack Parker-Holder; Risto Vuorio; Chris Lu 001; Gregory Farquhar; Shimon Whiteson; Jakob N. Foerster (NeurIPS, 2023)

Synthetic Experience Replay. - Cong Lu; Philip J. Ball; Yee Whye Teh; Jack Parker-Holder (NeurIPS, 2023)

Human-Timescale Adaptation in an Open-Ended Task Space. - Adaptive Agent Team; Jakob Bauer; Kate Baumli; Satinder Baveja; Feryal M. P. Behbahani; Avishkar Bhoopchand; Nathalie Bradley-Schmieg; Michael Chang; Natalie Clay; Adrian Collister; Vibhavari Dasagi; Lucy Gonzalez; et al. (CoRR, 2023)

Synthetic Experience Replay. - Cong Lu; Philip J. Ball; Jack Parker-Holder (CoRR, 2023)

Vision-Language Models as a Source of Rewards. - Kate Baumli; Satinder Baveja; Feryal M. P. Behbahani; Harris Chan; Gheorghe Comanici; Sebastian Flennerhag; Maxime Gazeau; Kristian Holsheimer; Dan Horgan; Michael Laskin; Clare Lyle; Hussain Masoom; et al. (CoRR, 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 (Trans. Mach. Learn. Res., 2023)

Multi-Agent Diagnostics for Robustness via Illuminated Diversity. - Mikayel Samvelyan; Davide Paglieri; Minqi Jiang; Jack Parker-Holder; Tim Rocktäschel (AAMAS, 2024)

Position: Open-Endedness is Essential for Artificial Superhuman Intelligence. - Edward Hughes 001; Michael D. Dennis; Jack Parker-Holder; Feryal M. P. Behbahani; Aditi Mavalankar; Yuge Shi; Tom Schaul; Tim Rocktäschel (ICML, 2024)

Genie: Generative Interactive Environments. - Jake Bruce; Michael D. Dennis; Ashley Edwards; Jack Parker-Holder; Yuge Shi; Edward Hughes 001; Matthew Lai; Aditi Mavalankar; Richie Steigerwald; Chris Apps; Yusuf Aytar; Sarah Bechtle; et al. (ICML, 2024)

Position: Video as the New Language for Real-World Decision Making. - Sherry Yang 001; Jacob C. Walker; Jack Parker-Holder; Yilun Du; Jake Bruce; André Barreto 001; Pieter Abbeel; Dale Schuurmans (ICML, 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 N. Foerster; Tim Rocktäschel; Roberta Raileanu (NeurIPS, 2024)

Video as the New Language for Real-World Decision Making. - Sherry Yang 001; Jacob C. Walker; Jack Parker-Holder; Yilun Du; Jake Bruce; André Barreto 001; Pieter Abbeel; Dale Schuurmans (CoRR, 2024)

Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs. - Davide Paglieri; Saurabh Dash; Tim Rocktäschel; Jack Parker-Holder (CoRR, 2024)

Open-Endedness is Essential for Artificial Superhuman Intelligence. - Edward Hughes 001; Michael Dennis 001; Jack Parker-Holder; Feryal M. P. Behbahani; Aditi Mavalankar; Yuge Shi; Tom Schaul; Tim Rocktäschel (CoRR, 2024)

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games. - Davide Paglieri; Bartlomiej Cupial; Samuel Coward; Ulyana Piterbarg; Maciej Wolczyk; Akbir Khan; Eduardo Pignatelli; Lukasz Kucinski; Lerrel Pinto; Rob Fergus; Jakob Nicolaus Foerster; Jack Parker-Holder; et al. (ICLR, 2025)

Synthetic Data is Sufficient for Zero-Shot Visual Generalization from Offline Data. - Ahmet Hamdi Güzel; Ilija Bogunovic; Jack Parker-Holder (Trans. Mach. Learn. Res., 2025)

Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents. - Davide Paglieri; Bartlomiej Cupial; Jonathan Cook 004; Ulyana Piterbarg; Jens Tuyls; Edward Grefenstette; Jakob Nicolaus Foerster; Jack Parker-Holder; Tim Rocktäschel (CoRR, 2025)

Imagined Autocurricula. - Ahmet Hamdi Güzel; Matthew Thomas Jackson; Jarek Liesen; Tim Rocktäschel; Jakob Nicolaus Foerster; Ilija Bogunovic; Jack Parker-Holder (CoRR, 2025)