End-to-end optical backpropagation for training neural networks. - Xianxin Guo; Thomas D. Barrett; Zhiming M. Wang; A. I. Lvovsky 001 (CoRR, 2019)

Exploratory Combinatorial Optimization with Reinforcement Learning. - Thomas D. Barrett; William R. Clements; Jakob N. Foerster; A. I. Lvovsky 001 (AAAI, 2020)

Learning Disentangled Representations and Group Structure of Dynamical Environments. - Robin Quessard; Thomas D. Barrett; William R. Clements (NeurIPS, 2020)

Learning Group Structure and Disentangled Representations of Dynamical Environments. - Robin Quessard; Thomas D. Barrett; William R. Clements (CoRR, 2020)

One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning. - Clément Bonnet; Paul Caron; Thomas D. Barrett; Ian Davies; Alexandre Laterre (CoRR, 2021)

Universally Expressive Communication in Multi-Agent Reinforcement Learning. - Matthew Morris; Thomas D. Barrett; Arnu Pretorius (NeurIPS, 2022)

Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration. - Thomas D. Barrett; Christopher W. F. Parsonson; Alexandre Laterre (CoRR, 2022)

Population-Based Reinforcement Learning for Combinatorial Optimization. - Nathan Grinsztajn; Daniel Furelos-Blanco; Thomas D. Barrett (CoRR, 2022)

Autoregressive neural-network wavefunctions for ab initio quantum chemistry. - Thomas D. Barrett; Aleksei O. Malyshev; A. I. Lvovsky 001 (Nat. Mach. Intell., 2022)

Reinforcement Learning for Branch-and-Bound Optimisation Using Retrospective Trajectories. - Christopher W. F. Parsonson; Alexandre Laterre; Thomas D. Barrett (AAAI, 2023)

Combinatorial Optimization with Policy Adaptation using Latent Space Search. - Félix Chalumeau; Shikha Surana; Clément Bonnet; Nathan Grinsztajn; Arnu Pretorius; Alexandre Laterre; Thomas D. Barrett (CoRR, 2023)

Are we going MAD? Benchmarking Multi-Agent Debate between Language Models for Medical Q&A. - Andries P. Smit; Paul Duckworth; Nathan Grinsztajn; Kale-ab Abebe Tessera; Thomas D. Barrett; Arnu Pretorius (CoRR, 2023)

ManyFold: an efficient and flexible library for training and validating protein folding models. - Amelia Villegas-Morcillo; Louis Robinson; Arthur Flajolet; Thomas D. Barrett (Bioinform., 2023)

Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs. - Andries P. Smit; Nathan Grinsztajn; Paul Duckworth; Thomas D. Barrett; Arnu Pretorius (ICML, 2024)

Learning the Language of Protein Structure. - Benoit Gaujac; Jérémie Donà; Liviu Copoiu; Timothy Atkinson 003; Thomas Pierrot; Thomas D. Barrett (CoRR, 2024)

Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization. - Félix Chalumeau; Refiloe Shabe; Noah de Nicola; Arnu Pretorius; Thomas D. Barrett; Nathan Grinsztajn (CoRR, 2024)

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps. - Lars L. Schaaf; Ilyes Batatia; Christoph Brunken; Thomas D. Barrett; Jules Tilly (CoRR, 2024)

Bootstrap Your Own Teacher: Online Policy Distillation for Multi-Game Reinforcement Learning. - Donal Byrne; Marko Tot; Paul Duckworth; Clément Bonnet; Alexandre Laterre; Thomas D. Barrett (CoG, 2025)

Metalic: Meta-Learning In-Context with Protein Language Models. - Jacob Beck; Shikha Surana; Manus McAuliffe; Oliver Bent; Thomas D. Barrett; Juan Jose Garau-Luis; Paul Duckworth (ICLR, 2025)

Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking. - Shikha Surana; Nathan Grinsztajn; Timothy Atkinson 003; Paul Duckworth; Thomas D. Barrett (CoRR, 2025)