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A Survey of Methods for Explaining Black Box Models

ACM computing surveys, 2018-08, Vol.51 (5), p.1-42, Article 93

2018

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Counterfactual explanations and how to find them: literature review and benchmarking

Data mining and knowledge discovery, 2024-09, Vol.38 (5), p.2770-2824

Guidotti, Riccardo

2024

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Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

IEEE access, 2022, Vol.10, p.100700-100724

2022

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Evaluating local explanation methods on ground truth

Artificial intelligence, 2021-02, Vol.291, p.103428, Article 103428

Guidotti, Riccardo

2021

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Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields

IEEE access, 2024, Vol.12, p.137893-137912

2024

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Matrix Profile-Based Interpretable Time Series Classifier

Frontiers in artificial intelligence, 2021-10, Vol.4, p.699448-699448

2021

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Human migration: the big data perspective

International Journal of Data Science and Analytics, 2021-05, Vol.11 (4), p.341-360

2021

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Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification

Diagnostics (Basel), 2024-04, Vol.14 (7), p.753

2024

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Discovering temporal regularities in retail customers’ shopping behavior

EPJ data science, 2018-12, Vol.7 (1), p.6-26, Article 6

2018

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Ensemble of Counterfactual Explainers

Discovery Science, 2021, Vol.12986, p.358-368

2021

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Give more data, awareness and control to individual citizens, and they will help COVID-19 containment

Ethics and information technology, 2021, Vol.23 (Suppl 1), p.1-6

2021

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Personal Analytics and Privacy. An Individual and Collective Perspective: First International Workshop, PAP 2017, Held in Conjunction with ECML PKDD 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers

1st Edition 2017, 2017

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Exploiting auto-encoders for explaining black-box classifiers

Intelligenza artificiale, 2022-07, Vol.16 (1), p.115-129

Guidotti, Riccardo

2022

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Factual and Counterfactual Explanations for Black Box Decision Making

IEEE intelligent systems, 2019-11, Vol.34 (6), p.14-23

2019

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Explaining Siamese networks in few-shot learning

Machine learning, 2024-10, Vol.113 (10), p.7723-7760

2024

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Solving imbalanced learning with outlier detection and features reduction

Machine learning, 2024-08, Vol.113 (8), p.5273-5330

2024

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Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

Information fusion, 2023-11, Vol.99, p.101805, Article 101805

2023

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Benchmarking and survey of explanation methods for black box models

Data mining and knowledge discovery, 2023-09, Vol.37 (5), p.1719-1778

2023

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GLocalX - From Local to Global Explanations of Black Box AI Models

Artificial intelligence, 2021-05, Vol.294, p.103457, Article 103457

2021

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ECML PKDD 2020 Workshops: Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14-18, 2020, Proceedings

2021

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Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences

IEEE transactions on knowledge and data engineering, 2019-11, Vol.31 (11), p.2151-2163

2019

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ECML PKDD 2018 Workshops: MIDAS 2018 and PAP 2018, Dublin, Ireland, September 10-14, 2018, Proceedings

1st ed. 2019., 2019

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Explaining short text classification with diverse synthetic exemplars and counter-exemplars

Machine learning, 2023-11, Vol.112 (11), p.4289-4322

2023

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A Survey of Methods for Explaining Black Box Models

Counterfactual explanations and how to find them: literature review and benchmarking

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

Evaluating local explanation methods on ground truth

Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields

Formal Methods. FM 2019 International Workshops: Porto, Portugal, October 7-11, 2019, Revised Selected Papers, Part II

Matrix Profile-Based Interpretable Time Series Classifier

Formal Methods. FM 2019 International Workshops: Porto, Portugal, October 7-11, 2019, Revised Selected Papers, Part I

Human migration: the big data perspective

Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification

Discovering temporal regularities in retail customers’ shopping behavior

Ensemble of Counterfactual Explainers

Give more data, awareness and control to individual citizens, and they will help COVID-19 containment

Personal Analytics and Privacy. An Individual and Collective Perspective: First International Workshop, PAP 2017, Held in Conjunction with ECML PKDD 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers

Exploiting auto-encoders for explaining black-box classifiers

Factual and Counterfactual Explanations for Black Box Decision Making

Explaining Siamese networks in few-shot learning

Solving imbalanced learning with outlier detection and features reduction

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

Benchmarking and survey of explanation methods for black box models

GLocalX - From Local to Global Explanations of Black Box AI Models

ECML PKDD 2020 Workshops: Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14-18, 2020, Proceedings

Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences

ECML PKDD 2018 Workshops: MIDAS 2018 and PAP 2018, Dublin, Ireland, September 10-14, 2018, Proceedings

Explaining short text classification with diverse synthetic exemplars and counter-exemplars

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