Selected Publications

This page shares some of my favorite papers, grouped into themes. See my Google Scholar for an up-to-date, comprehensive list.

Scaling

T Pearce, J Song
Reconciling Kaplan and Chinchilla Scaling Laws
TMLR 2024
Paper | Code

T Pearce*, T Rashid*, D Bignell, R Georgescu, S Devlin, K Hofmann
Scaling Laws for Pre-training Agents and World Models
ArXiv 2024
Paper

Imitation Learning

T Pearce, T Rashid, A Kanervisto, D Bignell, M Sun, R Georgescu, SV Macua, SZ Tan, I Momennejad, K Hofmann, S Devlin
Imitating Human Behaviour with Diffusion Models
ICLR 2023
Paper | Code

T Pearce, J Zhu
Counter-Strike Deathmatch with Large-Scale Behavioural Cloning
IEEE Conference on Games 2022, Best Paper Award
Paper | Video Intro | Code

T Luo, T Pearce, H Chen, J Chen, J Zhu
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory
NeurIPS 2024
Paper

World Models

E Alonso*, A Jelley*, V Micheli, A Kanervisto, A Storkey, T Pearce*, F Fleuret*
Diffusion for World Modeling: Visual Details Matter in Atari
NeurIPS 2024
Paper Code

Reinforcement Learning

F Lin*, S Huang*, T Pearce, W Chen, W-W Tu
TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play
AAMAS 2023
Paper

Uncertainty and Neural Networks

T Pearce, JH Jeong, Y Jia, J Zhu
Censored Quantile Regression Neural Networks
NeurIPS 2022
Paper | Code

T Pearce, A Brintrup, J Zhu
Understanding Softmax Confidence and Uncertainty
ArXiv 2021
Paper

R Tsuchida, T Pearce, C Van Der Heide, F Roosta, M Gallagher
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks
AAAI 2021
Paper

T Pearce, F Leibfried, M Zaki, A Brintrup, A Neely
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
AISTATS 2021
Paper | Video Intro | Interactive Demo | Code

T Pearce, R Tsuchida, M Zaki, A Brintrup, A Neely
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions
UAI 2019
Paper | Video Intro | Code

T Pearce, M Zaki, A Brintrup, A Neely
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
ICML 2018
Paper | Video Intro | Code