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Marco Tulio Ribeiro
Marco Tulio Ribeiro
Microsoft Research
Zweryfikowany adres z cs.washington.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
" 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
108012016
Anchors: High-Precision Model-Agnostic Explanations
MT Ribeiro, S Singh, C Guestrin
AAAI, 2018
13922018
Model-agnostic interpretability of machine learning
MT Ribeiro, S Singh, C Guestrin
arXiv preprint arXiv:1606.05386, 2016
7232016
Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
MT Ribeiro, T Wu, C Guestrin, S Singh
Association for Computational Linguistics (ACL), 2020
5442020
Semantically Equivalent Adversarial Rules for Debugging NLP Models
MT Ribeiro, S Singh, C Guestrin
Association for Computational Linguistics (ACL), 2018
3742018
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
1692021
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
1352012
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
1282014
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
912019
Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models
T Wu, MT Ribeiro, J Heer, DS Weld
arXiv preprint arXiv:2101.00288, 2021
86*2021
Are red roses red? evaluating consistency of question-answering models
MT Ribeiro, C Guestrin, S Singh
Association for Computational Linguistics (ACL), 2019
732019
Nothing else matters: Model-agnostic explanations by identifying prediction invariance
MT Ribeiro, S Singh, C Guestrin
arXiv preprint arXiv:1611.05817, 2016
662016
Programs as black-box explanations
S Singh, MT Ribeiro, C Guestrin
arXiv preprint arXiv:1611.07579, 2016
592016
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
42*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
342022
" 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
292016
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
262020
Finding and fixing spurious patterns with explanations
G Plumb, MT Ribeiro, A Talwalkar
arXiv preprint arXiv:2106.02112, 2021
82021
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
82013
ExSum: From Local Explanations to Model Understanding
Y Zhou, MT Ribeiro, J Shah
arXiv preprint arXiv:2205.00130, 2022
72022
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