Joel Jennings

According to our database, Joel Jennings authored at least 18 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Deep End-to-end Causal Inference.

Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Agrin Hilmkil, Joel Jennings, Meyer Scetbon, Miltiadis Allamanis, Cheng Zhang

Trans. Mach. Learn. Res., 2024

FiP: a Fixed-Point Approach for Causal Generative Modeling.

Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma

CoRR, 2024

Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention.

Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Fixed-Point Approach for Causal Generative Modeling.

Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Neural structure learning with stochastic differential equations.

Benjie Wang, Joel Jennings, Wenbo Gong

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Towards Causal Foundation Model: on Duality between Causal Inference and Attention.

Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma

CoRR, 2023

BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery.

Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong

CoRR, 2023

Understanding Causality with Large Language Models: Feasibility and Opportunities.

Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan

CoRR, 2023

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery.

Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design.

Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster

Proceedings of the International Conference on Machine Learning, 2023

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning.

Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise.

Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Instructions and Guide: Causal Insights for Learning Paths in Education.

Wenbo Gong, Digory Smith, Zichao Wang, Craig Barton, Simon Woodhead, Nick Pawlowski, Joel Jennings, Cheng Zhang

CoRR, 2022

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation.

Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster

CoRR, 2022

2021

DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention.

David Mguni, Joel Jennings, Taher Jafferjee, Aivar Sootla, Yaodong Yang, Changmin Yu, Usman Islam, Ziyan Wang, Jun Wang

CoRR, 2021

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

Proceedings of the 38th International Conference on Machine Learning, 2021

2019

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

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018

Decentralised Learning in Systems With Many, Many Strategic Agents.

David Mguni, Joel Jennings, Enrique Munoz de Cote

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018