| 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 | 100 | 2023 |
| 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 | 55 | 2023 |
| bnlearn: Bayesian network structure learning, parameter learning and inference M Scutari, R Ness R package version 3, 805, 2012 | 45 | 2012 |
| 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 | 41 | 2017 |
| 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 | 25 | 2016 |
| Package ‘bnlearn’: Bayesian network structure learning, parameter learning and inference M Scutari, R Ness Bayesian Network Structure Learning, Parameter Learning and Inference, 2016 | 16 | 2016 |
| 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 | 15 | 2024 |
| 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 | 15 | 2021 |
| Can generalist foundation models outcompete special-purpose tuning H Nori, YT Lee, S Zhang, D Carignan, R Edgar, N Fusi, N King, J Larson, ... Case study in medicine. arXiv preprint arXiv 231116452, 2023 | 11 | 2023 |
| 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 | 8 | 2023 |
| 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 | 7 | 2022 |
| 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 | 6 | 2019 |
| 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 | 5 | 2023 |
| Bayesian Network Structure Learning M Scutari, R Ness Parameter Learning and Inference, version 4, 2019 | 5 | 2019 |
| 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 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 | 4 | 2022 |
| Do-calculus enables causal reasoning with latent variable models S Mohammad-Taheri, R Ness, J Zucker, O Vitek | 2 | 2021 |
| Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science S Sharma, S Sharma, L Liu, R Tushir, A Neal, R Ness, J Crawford, ... arXiv preprint arXiv:2306.09302, 2023 | 1 | 2023 |
| Bayesian causal inference of cell signal transduction from proteomics experiments RDO Ness II Purdue University, 2016 | 1 | 2016 |
Olga VitekProfessor, Khoury College of Computer Sciences, Northeastern Universityneu.edu의 이메일 확인됨
