Decentralised Learning in Systems With Many, Many Strategic Agents. - David Mguni; Joel Jennings; Enrique Munoz de Cote (AAAI, 2018)

Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems. - David Mguni; Joel Jennings; Emilio Sison; Sergio Valcarcel Macua; Sofia Ceppi; Enrique Munoz de Cote (AAMAS, 2019)

Learning in Nonzero-Sum Stochastic Games with Potentials. - David Henry Mguni; Yutong Wu 005; Yali Du 001; Yaodong Yang 001; Ziyi Wang 004; Minne Li; Ying Wen 001; Joel Jennings; Jun Wang 012 (ICML, 2021)

DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention. - David Mguni; Joel Jennings; Taher Jafferjee; Aivar Sootla; Yaodong Yang 001; Changmin Yu; Usman Islam; Ziyan Wang; Jun Wang 012 (CoRR, 2021)

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation. - Desi R. Ivanova; Joel Jennings; Cheng Zhang 005; Adam Foster 001 (CoRR, 2022)

Instructions and Guide: Causal Insights for Learning Paths in Education. - Wenbo Gong 001; Digory Smith; Zichao Wang 001; Craig Barton; Simon Woodhead 002; Nick Pawlowski; Joel Jennings; Cheng Zhang 005 (CoRR, 2022)

Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. - Wenbo Gong 001; Joel Jennings; Cheng Zhang 005; Nick Pawlowski (ICLR, 2023)

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. - Matthew Ashman; Chao Ma 019; Agrin Hilmkil; Joel Jennings; Cheng Zhang 005 (ICLR, 2023)

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. - Desi R. Ivanova; Joel Jennings; Tom Rainforth; Cheng Zhang 005; Adam Foster 001 (ICML, 2023)

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery. - Yashas Annadani; Nick Pawlowski; Joel Jennings; Stefan Bauer; Cheng Zhang 005; Wenbo Gong 001 (NeurIPS, 2023)

Understanding Causality with Large Language Models: Feasibility and Opportunities. - Cheng Zhang 005; Stefan Bauer; Paul Bennett; Jiangfeng Gao; Wenbo Gong 001; Agrin Hilmkil; Joel Jennings; Chao Ma 019; Tom Minka; Nick Pawlowski; James Vaughan (CoRR, 2023)

BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery. - Yashas Annadani; Nick Pawlowski; Joel Jennings; Stefan Bauer; Cheng Zhang 005; Wenbo Gong 001 (CoRR, 2023)

Towards Causal Foundation Model: on Duality between Causal Inference and Attention. - Jiaqi Zhang; Joel Jennings; Cheng Zhang 005; Chao Ma 019 (CoRR, 2023)

Neural structure learning with stochastic differential equations. - Benjie Wang 001; Joel Jennings; Wenbo Gong 001 (ICLR, 2024)

A Fixed-Point Approach for Causal Generative Modeling. - Meyer Scetbon; Joel Jennings; Agrin Hilmkil; Cheng Zhang 005; Chao Ma 019 (ICML, 2024)

Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention. - Jiaqi Zhang; Joel Jennings; Agrin Hilmkil; Nick Pawlowski; Cheng Zhang 005; Chao Ma 019 (ICML, 2024)

FiP: a Fixed-Point Approach for Causal Generative Modeling. - Meyer Scetbon; Joel Jennings; Agrin Hilmkil; Cheng Zhang 005; Chao Ma 019 (CoRR, 2024)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Agrin Hilmkil; Joel Jennings; et al. (Trans. Mach. Learn. Res., 2024)