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Ana Lucic
Ana Lucic
Microsoft Research Amsterdam
Verified email at microsoft.com - Homepage
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Cited by
Year
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
A Lucic, M ter Hoeve, G Tolomei, M de Rijke, F Silvestri
Proceedings of the International Conference on Artificial Intelligence and …, 2022
502022
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
A Lucic, H Oosterhuis, H Haned, M de Rijke
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-22), 2022
422022
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting
A Lucic, H Haned, M de Rijke
Proceedings of the ACM Conference on Fairness, Accountability, and …, 2020
392020
FACTS-IR: Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval
A Olteanu, J Garcia-Gathright, M de Rijke, MD Ekstrand, A Roegiest, ...
ACM SIGIR Forum 53 (2), 20-43, 2021
242021
Order in the Court: Explainable AI Methods Prone to Disagreement
M Neely, SF Schouten, MJR Bleeker, A Lucic
ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend …, 2021
142021
To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions
K de Bie, A Lucic, H Haned
ICML 2021 Workshop on Human in the Loop Learning, 2021
92021
Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence
A Lucic, M Bleeker, S Jullien, S Bhargav, M de Rijke
Proceedings of the AAAI Symposium on Educational Advances in AI (EAAI-22), 2022
4*2022
A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms
A Lucic, M Srikumar, U Bhatt, A Xiang, A Taly, QV Liao, M de Rijke
CHI 2021 Workshop on Operationalizing Human-Centred Perspectives in …, 2021
42021
Explaining Predictions from Tree-based Boosting Ensembles
A Lucic, H Haned, M de Rijke
SIGIR 2019 Workshop on Fairness, Accountability, Confidentiality …, 2019
42019
A Song of (Dis) agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language Processing
M Neely, SF Schouten, M Bleeker, A Lucic
Proceedings of the International Conference on Hybrid Human-Artificial …, 2022
32022
Contrastive Explanations for Large Errors in Retail Forecasting Predictions through Monte Carlo Simulations
A Lucic, H Haned, M de Rijke
IJCAI 2019 Workshop on Explainable Artificial Intelligence, 2019
32019
Towards reproducible machine learning research in natural language processing
A Lucic, M Bleeker, S Bhargav, J Forde, K Sinha, J Dodge, S Luccioni, ...
Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022
22022
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
A Lucic, S Ahmad, AF Brinhosa, QV Liao, H Agrawal, U Bhatt, ...
ICML 2022 Workshop on Interpretable ML in Healthcare, 2022
12022
Towards Reproducible Machine Learning Research in Information Retrieval
A Lucic, M Bleeker, M de Rijke, K Sinha, S Jullien, R Stojnic
Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022
12022
Explaining Predictions from Machine Learning Models: Algorithms, Users, and Pedagogy
A Lucic
University of Amsterdam, 2022
2022
XAI Toolsheet: Towards A Documentation Framework for XAI Tools
S Karunagaran, A Lucic, C Custis
IJCAI 2022 Workshop on XAI, 2022
2022
ACL Tutorial Proposal: Towards Reproducible Machine Learning Research in Natural Language Processing
A Lucic, M Bleeker, S Bhargav, JZ Forde, K Sinha, J Dodge, S Luccioni, ...
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