The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. - Niki Kilbertus; Philip J. Ball; Matt J. Kusner; Adrian Weller; Ricardo Silva 001 (UAI, 2019)

Demystifying active inference. - Noor Sajid; Philip J. Ball; Karl J. Friston (CoRR, 2019)

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

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

A Study on Efficiency in Continual Learning Inspired by Human Learning. - Philip J. Ball; Yingzhen Li; Angus Lamb; Cheng Zhang 005 (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)

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

OffCon3: What is state of the art anyway? - Philip J. Ball; Stephen J. Roberts (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)

Active Inference: Demystified and Compared. - Noor Sajid; Philip J. Ball; Thomas Parr; Karl J. Friston (Neural Comput., 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)

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

Stabilizing Off-Policy Deep Reinforcement Learning from Pixels. - Edoardo Cetin; Philip J. Ball; Stephen J. Roberts; Oya Çeliktutan (ICML, 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)

Efficient Online Reinforcement Learning with Offline Data. - Philip J. Ball; Laura M. Smith; Ilya Kostrikov; Sergey Levine (ICML, 2023)

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

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

D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning. - Rafael Rafailov; Kyle Beltran Hatch; Anikait Singh; Aviral Kumar; Laura M. Smith; Ilya Kostrikov; Philippe Hansen-Estruch; Victor Kolev; Philip J. Ball; Jiajun Wu 001; Sergey Levine; Chelsea Finn (RLJ, 2024)