| " 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 | 11516 | 2016 |
| Anchors: High-Precision Model-Agnostic Explanations MT Ribeiro, S Singh, C Guestrin AAAI, 2018 | 1490 | 2018 |
| Model-agnostic interpretability of machine learning MT Ribeiro, S Singh, C Guestrin arXiv preprint arXiv:1606.05386, 2016 | 769 | 2016 |
| Beyond Accuracy: Behavioral Testing of NLP Models with CheckList MT Ribeiro, T Wu, C Guestrin, S Singh Association for Computational Linguistics (ACL), 2020 | 589 | 2020 |
| Semantically Equivalent Adversarial Rules for Debugging NLP Models MT Ribeiro, S Singh, C Guestrin Association for Computational Linguistics (ACL), 2018 | 385 | 2018 |
| 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 | 186 | 2021 |
| 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 | 137 | 2012 |
| 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 | 127 | 2014 |
| Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models T Wu, MT Ribeiro, J Heer, DS Weld arXiv preprint arXiv:2101.00288, 2021 | 96* | 2021 |
| 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 | 93 | 2019 |
| Are red roses red? evaluating consistency of question-answering models MT Ribeiro, C Guestrin, S Singh Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 74 | 2019 |
| Nothing else matters: Model-agnostic explanations by identifying prediction invariance MT Ribeiro, S Singh, C Guestrin arXiv preprint arXiv:1611.05817, 2016 | 69 | 2016 |
| Programs as black-box explanations S Singh, MT Ribeiro, C Guestrin arXiv preprint arXiv:1611.07579, 2016 | 57 | 2016 |
| Why Should I Trust You?": Explaining the Predictions of Any Classifier. CoRR abs/1602.04938 (2016) MT Ribeiro, S Singh, C Guestrin arXiv preprint arXiv:1602.04938, 2016 | 51 | 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 | 41 | 2020 |
| Do feature attribution methods correctly attribute features? Y Zhou, S Booth, MT Ribeiro, J Shah Proceedings of the AAAI Conference on Artificial Intelligence 36 (9), 9623-9633, 2022 | 38 | 2022 |
| 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 | 28 | 2020 |
| Exsum: From local explanations to model understanding Y Zhou, MT Ribeiro, J Shah arXiv preprint arXiv:2205.00130, 2022 | 10 | 2022 |
| Finding and fixing spurious patterns with explanations G Plumb, MT Ribeiro, A Talwalkar arXiv preprint arXiv:2106.02112, 2021 | 10 | 2021 |
| Adaptive testing and debugging of nlp models MT Ribeiro, S Lundberg Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 8 | 2022 |
Carlos GuestrinProfessor, Stanford UniversityE-mailová adresa ověřena na: stanford.edu
