| 2024 | ICLR | Neural structure learning with stochastic differential equations. | Benjie Wang, Joel Jennings, Wenbo Gong |
| 2024 | ICML | A Fixed-Point Approach for Causal Generative Modeling. | Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma |
| 2024 | ICML | Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention. | Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma |
| 2023 | ICLR | Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. | Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski |
| 2023 | ICLR | Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. | Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang |
| 2023 | ICML | CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. | Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster |
| 2021 | ICML | Learning in Nonzero-Sum Stochastic Games with Potentials. | David Henry Mguni, Yutong Wu, Yali Du, Yaodong Yang, Ziyi Wang, Minne Li, Ying Wen, Joel Jennings, Jun Wang |
| 2018 | AAAI | Decentralised Learning in Systems With Many, Many Strategic Agents. | David Mguni, Joel Jennings, Enrique Munoz de Cote |