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ORCIDJoel Jennings
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2020 – today
- 2024
[j1]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)- [c10]Benjie Wang, Joel Jennings, Wenbo Gong:
Neural structure learning with stochastic differential equations. ICLR 2024 - [c9]Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma:
A Fixed-Point Approach for Causal Generative Modeling. ICML 2024 - [c8]Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma:
Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention. ICML 2024 - [i14]Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma:
FiP: a Fixed-Point Approach for Causal Generative Modeling. CoRR abs/2404.06969 (2024) - 2023
- [c7]Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski:
Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. ICLR 2023 - [c6]Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang:
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. ICLR 2023 - [c5]Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster:
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. ICML 2023: 14445-14464 - [c4]Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong:
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery. NeurIPS 2023 - [i13]Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster:
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. CoRR abs/2302.14015 (2023) - [i12]Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang:
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. CoRR abs/2303.12703 (2023) - [i11]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 abs/2304.05524 (2023) - [i10]Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong:
BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery. CoRR abs/2307.13917 (2023) - [i9]Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma:
Towards Causal Foundation Model: on Duality between Causal Inference and Attention. CoRR abs/2310.00809 (2023) - [i8]Benjie Wang, Joel Jennings, Wenbo Gong:
Neural Structure Learning with Stochastic Differential Equations. CoRR abs/2311.03309 (2023) - 2022
- [i7]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 abs/2207.05250 (2022) - [i6]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 abs/2208.12610 (2022) - [i5]Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski:
Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise. CoRR abs/2210.14706 (2022) - 2021
- [c3]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: 7688-7699 - [i4]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 abs/2103.09284 (2021) - [i3]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 abs/2110.14468 (2021)
2010 – 2019
- 2019
- [c2]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: 386-394 - [i2]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 abs/1901.10923 (2019) - 2018
- [c1]David Mguni, Joel Jennings, Enrique Munoz de Cote:
Decentralised Learning in Systems With Many, Many Strategic Agents. AAAI 2018: 4686-4693 - [i1]David Mguni, Joel Jennings, Enrique Munoz de Cote:
Decentralised Learning in Systems with Many, Many Strategic Agents. CoRR abs/1803.05028 (2018)
Coauthor Index
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