| " Why Should I Trust You?": Explaining the Predictions of Any Classifier MT Ribeiro, S Singh, C Guestrin Knowledge Discovery and Data Mining (ACM KDD), 2016 | 9286 | 2016 |
| Anchors: High-Precision Model-Agnostic Explanations MT Ribeiro, S Singh, C Guestrin AAAI, 2018 | 1182 | 2018 |
| Model-agnostic interpretability of machine learning MT Ribeiro, S Singh, C Guestrin arXiv preprint arXiv:1606.05386, 2016 | 619 | 2016 |
| Beyond Accuracy: Behavioral Testing of NLP Models with CheckList MT Ribeiro, T Wu, C Guestrin, S Singh Association for Computational Linguistics (ACL), 2020 | 414 | 2020 |
| Semantically Equivalent Adversarial Rules for Debugging NLP Models MT Ribeiro, S Singh, C Guestrin Association for Computational Linguistics (ACL), 2018 | 336 | 2018 |
| Pareto-efficient hybridization for multi-objective recommender systems MT Ribeiro, A Lacerda, A Veloso, N Ziviani Proceedings of the sixth ACM conference on Recommender systems, 19-26, 2012 | 128 | 2012 |
| Does the whole exceed its parts? the effect of ai explanations on complementary team performance G Bansal, T Wu, J Zhou, R Fok, B Nushi, E Kamar, MT Ribeiro, D Weld Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021 | 115 | 2021 |
| Multiobjective pareto-efficient approaches for recommender systems MT Ribeiro, N Ziviani, ESD Moura, I Hata, A Lacerda, A Veloso ACM Transactions on Intelligent Systems and Technology (TIST) 5 (4), 1-20, 2014 | 113 | 2014 |
| Errudite: Scalable, reproducible, and testable error analysis T Wu, MT Ribeiro, J Heer, DS Weld Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 75 | 2019 |
| Are red roses red? evaluating consistency of question-answering models MT Ribeiro, C Guestrin, S Singh Association for Computational Linguistics (ACL), 2019 | 65 | 2019 |
| Nothing else matters: Model-agnostic explanations by identifying prediction invariance MT Ribeiro, S Singh, C Guestrin arXiv preprint arXiv:1611.05817, 2016 | 63 | 2016 |
| Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models T Wu, MT Ribeiro, J Heer, DS Weld arXiv preprint arXiv:2101.00288, 2021 | 58* | 2021 |
| Programs as black-box explanations S Singh, MT Ribeiro, C Guestrin arXiv preprint arXiv:1611.07579, 2016 | 54 | 2016 |
| Squinting at vqa models: Introspecting vqa models with sub-questions RR Selvaraju, P Tendulkar, D Parikh, E Horvitz, MT Ribeiro, B Nushi, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 38* | 2020 |
| Do feature attribution methods correctly attribute features Y Zhou, S Booth, MT Ribeiro, J Shah arXiv preprint arXiv:2104.14403, 2021 | 22 | 2021 |
| Intelligible and explainable machine learning: best practices and practical challenges R Caruana, S Lundberg, MT Ribeiro, H Nori, S Jenkins Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 20 | 2020 |
| " Why Should I Trust You?": Explaining the Predictions of Any Classifier M Tulio Ribeiro, S Singh, C Guestrin ArXiv e-prints, arXiv: 1602.04938, 2016 | 16 | 2016 |
| A holistic hybrid algorithm for user recommendation on twitter S Guimarães, MT Ribeiro, R Assunção, W Meira Jr Journal of Information and Data Management 4 (3), 341-341, 2013 | 8 | 2013 |
| Spam detection using web page content: a new battleground MT Ribeiro, PHC Guerra, L Vilela, A Veloso, D Guedes, W Meira Jr, ... Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti …, 2011 | 6 | 2011 |
| Spam miner: a platform for detecting and characterizing spam campaigns PHC Guerra, DEV Pires, MTC Ribeiro, D Guedes, W Meira Jr, C Hoepers, ... Proc. 6th Conf. Email Anti-Spam, 2008 | 6 | 2008 |
Carlos GuestrinProfessor, Stanford UniversityE-mailová adresa ověřena na: stanford.edu
