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Robert Osazuwa Ness
Robert Osazuwa Ness
Microsoft Research
Dirección de correo verificada de altdeep.ai - Página principal
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Causal reasoning and large language models: Opening a new frontier for causality
E Kıcıman, R Ness, A Sharma, C Tan
arXiv preprint arXiv:2305.00050, 2023
1402023
Can generalist foundation models outcompete special-purpose tuning? case study in medicine
H Nori, YT Lee, S Zhang, D Carignan, R Edgar, N Fusi, N King, J Larson, ...
arXiv preprint arXiv:2311.16452, 2023
1272023
bnlearn: Bayesian network structure learning, parameter learning and inference
M Scutari, R Ness
R package version 3, 805, 2012
482012
A Bayesian active learning experimental design for inferring signaling networks
RO Ness, K Sachs, P Mallick, O Vitek
Research in Computational Molecular Biology: 21st Annual International …, 2017
422017
Evaluating cognitive maps and planning in large language models with CogEval
I Momennejad, H Hasanbeig, F Vieira Frujeri, H Sharma, N Jojic, ...
Advances in Neural Information Processing Systems 36, 2024
272024
From correlation to causality: statistical approaches to learning regulatory relationships in large-scale biomolecular investigations
RO Ness, K Sachs, O Vitek
Journal of Proteome Research 15 (3), 683-690, 2016
262016
Leveraging structured biological knowledge for counterfactual inference: a case study of viral pathogenesis
J Zucker, K Paneri, S Mohammad-Taheri, S Bhargava, P Kolambkar, ...
IEEE Transactions on Big Data 7 (1), 25-37, 2021
172021
Package ‘bnlearn’: Bayesian network structure learning, parameter learning and inference
M Scutari, R Ness
Bayesian Network Structure Learning, Parameter Learning and Inference, 2016
162016
Can generalist foundation models outcompete special-purpose tuning? Case study in medicine. arXiv
H Nori, YT Lee, S Zhang, D Carignan, R Edgar, N Fusi, N King, J Larson, ...
arXiv preprint arXiv:2311.16452, 2023
102023
Evaluating cognitive maps in large language models with cogeval: No emergent planning
I Momennejad, H Hasanbeig, FV Frujeri, H Sharma, RO Ness, N Jojic, ...
Advances in neural information processing systems 37, 2023
92023
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
92022
Can generalist foundation models outcompete special-purpose tuning? case study in medicine. Microsoft
H Nori, YT Lee, S Zhang, D Carignan, R Edgar, N Fusi, N King, J Larson, ...
72023
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
R Ness, K Paneri, O Vitek
Advances in Neural Information Processing Systems 32, 2019
72019
Causal reasoning and large language models: Opening a new frontier for causality. arXiv. 2023
E Kıcıman, R Ness, A Sharma, C Tan
arXiv preprint arXiv:2305.00050, 2023
62023
Do-calculus enables estimation of causal effects in partially observed biomolecular pathways
S Mohammad-Taheri, J Zucker, CT Hoyt, K Sachs, V Tewari, R Ness, ...
Bioinformatics 38 (Supplement_1), i350-i358, 2022
62022
Causal reasoning and large language models: Opening a new frontier for causality. arXiv
E Kıcıman, R Ness, A Sharma, C Tan
arXiv preprint arXiv:2305.00050, 2023
52023
Bayesian Network Structure Learning
M Scutari, R Ness
Parameter Learning and Inference, version 4, 2019
52019
Package ‘bnlearn’. 2019
M Scutari, R Ness
URL http://www. bnlearn. com, 0
5
bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference, 2018
M Scutari, R Ness
URL https://CRAN. R-project. org/package= bnlearn. R package version 4, 455, 0
5
Do-calculus enables causal reasoning with latent variable models
S Mohammad-Taheri, R Ness, J Zucker, O Vitek
arXiv preprint arXiv:2102.06626, 2021
22021
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
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