| " 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 | 19455 | 2016 |
| Sparks of artificial general intelligence: Early experiments with gpt-4 S Bubeck, V Chandrasekaran, R Eldan, J Gehrke, E Horvitz, E Kamar, ... arXiv preprint arXiv:2303.12712, 2023 | 2815 | 2023 |
| Anchors: High-Precision Model-Agnostic Explanations MT Ribeiro, S Singh, C Guestrin AAAI, 2018 | 2458 | 2018 |
| Model-agnostic interpretability of machine learning MT Ribeiro, S Singh, C Guestrin arXiv preprint arXiv:1606.05386, 2016 | 1237 | 2016 |
| Beyond Accuracy: Behavioral Testing of NLP Models with CheckList MT Ribeiro, T Wu, C Guestrin, S Singh Association for Computational Linguistics (ACL), 2020 | 1060 | 2020 |
| 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 | 552 | 2021 |
| Semantically Equivalent Adversarial Rules for Debugging NLP Models MT Ribeiro, S Singh, C Guestrin Association for Computational Linguistics (ACL), 2018 | 531 | 2018 |
| Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv S Bubeck, V Chandrasekaran, R Eldan, J Gehrke, E Horvitz, E Kamar, ... arXiv preprint arXiv:2303.12712, 2023 | 305 | 2023 |
| Editing models with task arithmetic G Ilharco, MT Ribeiro, M Wortsman, S Gururangan, L Schmidt, ... arXiv preprint arXiv:2212.04089, 2022 | 269 | 2022 |
| Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models T Wu, MT Ribeiro, J Heer, DS Weld arXiv preprint arXiv:2101.00288, 2021 | 263* | 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 | 167 | 2014 |
| 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 | 165 | 2012 |
| why should i trust you?”: explaining the predictions of any classifier; 2016 MT Ribeiro, S Singh, C Guestrin arXiv preprint arXiv:1602.04938, 2019 | 153 | 2019 |
| 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 | 149 | 2019 |
| Art: Automatic multi-step reasoning and tool-use for large language models B Paranjape, S Lundberg, S Singh, H Hajishirzi, L Zettlemoyer, ... arXiv preprint arXiv:2303.09014, 2023 | 145 | 2023 |
| 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 | 141 | 2022 |
| 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 | 109 | 2019 |
| Nothing else matters: Model-agnostic explanations by identifying prediction invariance MT Ribeiro, S Singh, C Guestrin arXiv preprint arXiv:1611.05817, 2016 | 93 | 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 | 82 | 2016 |
| Adaptive testing and debugging of nlp models MT Ribeiro, S Lundberg Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 75 | 2022 |
Carlos GuestrinProfessor, Stanford UniversityVerified email at stanford.edu
