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Joel Jennings
Joel Jennings
Microsoft Research
Verified email at microsoft.com
Title
Cited by
Cited by
Year
Decentralised learning in systems with many, many strategic agents
D Mguni, J Jennings, EM de Cote
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
742018
Learning in nonzero-sum stochastic games with potentials
DH Mguni, Y Wu, Y Du, Y Yang, Z Wang, M Li, Y Wen, J Jennings, J Wang
International Conference on Machine Learning, 7688-7699, 2021
472021
Tumour heterogeneity promotes collective invasion and cancer metastatic dissemination
A Hallou, J Jennings, AJ Kabla
Royal Society Open Science 4 (8), 161007, 2017
412017
Coordinating the crowd: Inducing desirable equilibria in non-cooperative systems
D Mguni, J Jennings, SV Macua, E Sison, S Ceppi, EM De Cote
arXiv preprint arXiv:1901.10923, 2019
392019
Understanding causality with large language models: Feasibility and opportunities
C Zhang, S Bauer, P Bennett, J Gao, W Gong, A Hilmkil, J Jennings, C Ma, ...
arXiv preprint arXiv:2304.05524, 2023
252023
Machine learning system
A Tukiainen, D Kim, T Nicholson, M Tomczak, JEMDEC FLORES, ...
US Patent App. 16/753,580, 2020
242020
Rhino: Deep causal temporal relationship learning with history-dependent noise
W Gong, J Jennings, C Zhang, N Pawlowski
arXiv preprint arXiv:2210.14706, 2022
162022
A causal AI suite for decision-making
E Kiciman, EW Dillon, D Edge, A Foster, A Hilmkil, J Jennings, C Ma, ...
NeurIPS 2022 Workshop on Causality for Real-world Impact, 2022
72022
Answering causal questions with augmented llms
N Pawlowski, J Vaughan, J Jennings, C Zhang
62023
Causal reasoning in the presence of latent confounders via neural admg learning
M Ashman, C Ma, A Hilmkil, J Jennings, C Zhang
arXiv preprint arXiv:2303.12703, 2023
62023
A new computational model for multi-cellular biological systems
JN Jennings
62014
Towards causal foundation model: on duality between causal inference and attention
J Zhang, J Jennings, C Zhang, C Ma
arXiv preprint arXiv:2310.00809, 2023
52023
CO-BED: Information-theoretic contextual optimization via Bayesian experimental design
DR Ivanova, J Jennings, T Rainforth, C Zhang, A Foster
International Conference on Machine Learning, 14445-14464, 2023
52023
Desta: A framework for safe reinforcement learning with markov games of intervention
D Mguni, U Islam, Y Sun, X Zhang, J Jennings, A Sootla, C Yu, Z Wang, ...
arXiv preprint arXiv:2110.14468, 2021
52021
Bayesdag: Gradient-based posterior sampling for causal discovery
Y Annadani, N Pawlowski, J Jennings, S Bauer, C Zhang, W Gong
arXiv preprint arXiv:2307.13917, 2023
42023
Using neural biomarkers to personalize dosing of vagus nerve stimulation
A Berthon, L Wernisch, M Stoukidi, M Thornton, O Tessier-Lariviere, ...
bioRxiv, 2023.08. 30.555487, 2023
32023
Cancer Metastasis: collective invasion in heterogeneous multicellular systems
A Hallou, J Jennings, A Kabla
arXiv preprint arXiv:1501.00065, 2014
32014
Efficient real-world testing of causal decision making via bayesian experimental design for contextual optimisation
DR Ivanova, J Jennings, C Zhang, A Foster
arXiv preprint arXiv:2207.05250, 2022
22022
Instructions and guide: Causal insights for learning paths in education
W Gong, D Smith, Z Wang, C Barton, S Woodhead, N Pawlowski, ...
arXiv preprint arXiv:2208.12610, 2022
22022
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
Y Annadani, N Pawlowski, J Jennings, S Bauer, C Zhang, W Gong
Advances in Neural Information Processing Systems 36, 2024
12024
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