Pearce, Tim

15 publications

ICML 2025 Scaling Laws for Pre-Training Agents and World Models Tim Pearce, Tabish Rashid, David Bignell, Raluca Georgescu, Sam Devlin, Katja Hofmann

ICLRW 2025 Scaling Laws for Pre-Training Agents and World Models Tim Pearce, Tabish Rashid, David Bignell, Raluca Georgescu, Sam Devlin, Katja Hofmann

NeurIPS 2025 What Do Latent Action Models Actually Learn? Chuheng Zhang, Tim Pearce, Pushi Zhang, Kaixin Wang, Xiaoyu Chen, Wei Shen, Li Zhao, Jiang Bian

NeurIPS 2024 C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory Tianjiao Luo, Tim Pearce, Huayu Chen, Jianfei Chen, Jun Zhu

AAAI 2024 DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization Wentse Chen, Shiyu Huang, Yuan Chiang, Tim Pearce, Wei-Wei Tu, Ting Chen, Jun Zhu

NeurIPS 2024 Diffusion for World Modeling: Visual Details Matter in Atari Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto, Amos Storkey, Tim Pearce, François Fleuret

TMLR 2024 Reconciling Kaplan and Chinchilla Scaling Laws Tim Pearce, Jinyeop Song

NeurIPSW 2023 Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale? Stephen Mak, Tim Pearce, Matthew Macfarlane, Liming Xu, Michael Ostroumov, Alexandra Brintrup

ICLR 2023 Imitating Human Behaviour with Diffusion Models Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin

NeurIPS 2022 Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis Tim Pearce, Jong-Hyeon Jeong, Yichen Jia, Jun Zhu

NeurIPSW 2022 Imitating Human Behaviour with Diffusion Models Tim Pearce, Tabish Rashid, Anssi Kanervisto, David Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin

AAAI 2021 Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks Russell Tsuchida, Tim Pearce, Christopher van der Heide, Fred Roosta, Marcus Gallagher

AISTATS 2020 Uncertainty in Neural Networks: Approximately Bayesian Ensembling Tim Pearce, Felix Leibfried, Alexandra Brintrup

UAI 2019 Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions Tim Pearce, Russell Tsuchida, Mohamed Zaki, Alexandra Brintrup, Andy Neely

ICML 2018 High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach Tim Pearce, Alexandra Brintrup, Mohamed Zaki, Andy Neely