Deep Reinforcement Learning for Process Synthesis. - Laurence Illing Midgley (CoRR, 2020)

Bootstrap Your Flow. - Laurence Illing Midgley; Vincent Stimper; Gregor N. C. Simm; José Miguel Hernández-Lobato (CoRR, 2021)

Synthesis of separation processes with reinforcement learning. - Stephan C. P. A. van Kalmthout; Laurence Illing Midgley; Meik B. Franke (CoRR, 2022)

Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value Function. - Clément Bonnet; Laurence I. Midgley; Alexandre Laterre (CoRR, 2022)

Flow Annealed Importance Sampling Bootstrap. - Laurence Illing Midgley; Vincent Stimper; Gregor N. C. Simm; Bernhard Schölkopf; José Miguel Hernández-Lobato (ICLR, 2023)

SE(3) Equivariant Augmented Coupling Flows. - Laurence I. Midgley; Vincent Stimper; Javier Antorán; Emile Mathieu; Bernhard Schölkopf; José Miguel Hernández-Lobato (NeurIPS, 2023)

Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX. - Clément Bonnet; Daniel Luo; Donal Byrne; Shikha Surana; Vincent Coyette; Paul Duckworth; Laurence I. Midgley; Tristan Kalloniatis; Sasha Abramowitz; Cemlyn N. Waters; Andries P. Smit; Nathan Grinsztajn; et al. (CoRR, 2023)

Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX. - Clément Bonnet; Daniel Luo; Donal Byrne; Shikha Surana; Sasha Abramowitz; Paul Duckworth; Vincent Coyette; Laurence Illing Midgley; Elshadai Tegegn; Tristan Kalloniatis; Omayma Mahjoub; Matthew Macfarlane; et al. (ICLR, 2024)

Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models. - Fengzhe Zhang; Jiajun He 003; Laurence I. Midgley; Javier Antorán; José Miguel Hernández-Lobato (CoRR, 2024)

Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models. - Fengzhe Zhang; Laurence I. Midgley; José Miguel Hernández-Lobato (CoRR, 2025)