Joel Jennings

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 23

Top Topics

Experimental Design

Reinforcement Learning

Causal Inference

Top Venues

Publications

Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
FiP: a Fixed-Point Approach for Causal Generative Modeling. CoRR (2024)
Benjie Wang, Joel Jennings, Wenbo Gong
Neural structure learning with stochastic differential equations. ICLR (2024)
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
Deep End-to-end Causal Inference. Trans. Mach. Learn. Res. 2024 (2024)
Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery. NeurIPS (2023)
Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. CoRR (2023)
Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma
Towards Causal Foundation Model: on Duality between Causal Inference and Attention. CoRR (2023)
Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. CoRR (2023)
Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong
BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery. CoRR (2023)
Benjie Wang, Joel Jennings, Wenbo Gong
Neural Structure Learning with Stochastic Differential Equations. CoRR (2023)
Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. ICML (2023)
Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. ICLR (2023)
Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski
Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. ICLR (2023)
Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan
Understanding Causality with Large Language Models: Feasibility and Opportunities. CoRR (2023)
Wenbo Gong, Digory Smith, Zichao Wang, Craig Barton, Simon Woodhead, Nick Pawlowski, Joel Jennings, Cheng Zhang
Instructions and Guide: Causal Insights for Learning Paths in Education. CoRR (2022)
Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski
Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise. CoRR (2022)
Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster
Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation. CoRR (2022)
David Henry Mguni, Yutong Wu, Yali Du, Yaodong Yang, Ziyi Wang, Minne Li, Ying Wen, Joel Jennings, Jun Wang
Learning in Nonzero-Sum Stochastic Games with Potentials. ICML (2021)
David Mguni, Joel Jennings, Taher Jafferjee, Aivar Sootla, Yaodong Yang, Changmin Yu, Usman Islam, Ziyan Wang, Jun Wang
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention. CoRR (2021)
David Mguni, Yutong Wu, Yali Du, Yaodong Yang, Ziyi Wang, Minne Li, Ying Wen, Joel Jennings, Jun Wang
Learning in Nonzero-Sum Stochastic Games with Potentials. CoRR (2021)
David Mguni, Joel Jennings, Sergio Valcarcel Macua, Emilio Sison, Sofia Ceppi, Enrique Munoz de Cote
Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems. CoRR (2019)
David Mguni, Joel Jennings, Emilio Sison, Sergio Valcarcel Macua, Sofia Ceppi, Enrique Munoz de Cote
Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems. AAMAS (2019)
David Mguni, Joel Jennings, Enrique Munoz de Cote
Decentralised Learning in Systems with Many, Many Strategic Agents. CoRR (2018)
David Mguni, Joel Jennings, Enrique Munoz de Cote
Decentralised Learning in Systems With Many, Many Strategic Agents. AAAI (2018)