Marco Tulio Ribeiro
Marco Tulio Ribeiro
Microsoft Research
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Cited by
Cited by
" 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
Anchors: High-Precision Model-Agnostic Explanations
MT Ribeiro, S Singh, C Guestrin
AAAI, 2018
Model-agnostic interpretability of machine learning
MT Ribeiro, S Singh, C Guestrin
arXiv preprint arXiv:1606.05386, 2016
Semantically Equivalent Adversarial Rules for Debugging NLP Models
MT Ribeiro, S Singh, C Guestrin
Association for Computational Linguistics (ACL), 2018
Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
MT Ribeiro, T Wu, C Guestrin, S Singh
Association for Computational Linguistics (ACL), 2020
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
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
Nothing else matters: model-agnostic explanations by identifying prediction invariance
MT Ribeiro, S Singh, C Guestrin
arXiv preprint arXiv:1611.05817, 2016
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
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
Programs as black-box explanations
S Singh, MT Ribeiro, C Guestrin
arXiv preprint arXiv:1611.07579, 2016
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
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
T Wu, MT Ribeiro, J Heer, DS Weld
Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021
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
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
Do Feature Attribution Methods Correctly Attribute Features?
Y Zhou, S Booth, MT Ribeiro, J Shah
arXiv preprint arXiv:2104.14403, 2021
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
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
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
Multi-objective pareto-efficient algorithms for recommender systems
MTC Ribeiro
Universidade Federal de Minas Gerais, 2013
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