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Artykuły w czasopismach na temat "Counterfactual explanations"
Sia, Suzanna, Anton Belyy, Amjad Almahairi, Madian Khabsa, Luke Zettlemoyer i Lambert Mathias. "Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 8 (26.06.2023): 9837–45. http://dx.doi.org/10.1609/aaai.v37i8.26174.
Pełny tekst źródłaAsher, Nicholas, Lucas De Lara, Soumya Paul i Chris Russell. "Counterfactual Models for Fair and Adequate Explanations". Machine Learning and Knowledge Extraction 4, nr 2 (31.03.2022): 316–49. http://dx.doi.org/10.3390/make4020014.
Pełny tekst źródłaVanNostrand, Peter M., Huayi Zhang, Dennis M. Hofmann i Elke A. Rundensteiner. "FACET: Robust Counterfactual Explanation Analytics". Proceedings of the ACM on Management of Data 1, nr 4 (8.12.2023): 1–27. http://dx.doi.org/10.1145/3626729.
Pełny tekst źródłaKenny, Eoin M., i Mark T. Keane. "On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 13 (18.05.2021): 11575–85. http://dx.doi.org/10.1609/aaai.v35i13.17377.
Pełny tekst źródłaLeofante, Francesco, i Nico Potyka. "Promoting Counterfactual Robustness through Diversity". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 19 (24.03.2024): 21322–30. http://dx.doi.org/10.1609/aaai.v38i19.30127.
Pełny tekst źródłaDelaney, Eoin, Arjun Pakrashi, Derek Greene i Mark T. Keane. "Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ (Abstract Reprint)". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 20 (24.03.2024): 22696. http://dx.doi.org/10.1609/aaai.v38i20.30596.
Pełny tekst źródłaBaron, Sam, Mark Colyvan i David Ripley. "A Counterfactual Approach to Explanation in Mathematics". Philosophia Mathematica 28, nr 1 (2.12.2019): 1–34. http://dx.doi.org/10.1093/philmat/nkz023.
Pełny tekst źródłaSchleich, Maximilian, Zixuan Geng, Yihong Zhang i Dan Suciu. "GeCo". Proceedings of the VLDB Endowment 14, nr 9 (maj 2021): 1681–93. http://dx.doi.org/10.14778/3461535.3461555.
Pełny tekst źródłaBarzekar, Hosein, i Susan McRoy. "Achievable Minimally-Contrastive Counterfactual Explanations". Machine Learning and Knowledge Extraction 5, nr 3 (3.08.2023): 922–36. http://dx.doi.org/10.3390/make5030048.
Pełny tekst źródłaChapman-Rounds, Matt, Umang Bhatt, Erik Pazos, Marc-Andre Schulz i Konstantinos Georgatzis. "FIMAP: Feature Importance by Minimal Adversarial Perturbation". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 13 (18.05.2021): 11433–41. http://dx.doi.org/10.1609/aaai.v35i13.17362.
Pełny tekst źródłaRozprawy doktorskie na temat "Counterfactual explanations"
Jeyasothy, Adulam. "Génération d'explications post-hoc personnalisées". Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS027.
Pełny tekst źródłaThis thesis is in the field of eXplainable AI (XAI). We focus on post-hoc interpretability methods that aim to explain to a user the prediction for a specific data made by a trained decision model. To increase the interpretability of explanations, this thesis studies the integration of user knowledge into these methods, and thus aims to improve the understandability of the explanation by generating personalized explanations tailored to each user. To this end, we propose a general formalism that explicitly integrates knowledge via a new criterion in the interpretability objectives. This formalism is then declined for different types of knowledge and different types of explanations, particularly counterfactual examples, leading to the proposal of several algorithms (KICE, Knowledge Integration in Counterfactual Explanation, rKICE for its variant including knowledge expressed by rules and KISM, Knowledge Integration in Surrogate Models). The issue of aggregating classical quality and knowledge compatibility constraints is also studied, and we propose to use Gödel's integral as an aggregation operator. Finally, we discuss the difficulty of generating a single explanation suitable for all types of users and the notion of diversity in explanations
Broadbent, Alex. "A reverse counterfactual analysis of causation". Thesis, University of Cambridge, 2007. https://www.repository.cam.ac.uk/handle/1810/226170.
Pełny tekst źródłaKuo, Chia-Yu, i 郭家諭. "Explainable Risk Prediction System for Child Abuse Event by Individual Feature Attribution and Counterfactual Explanation". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/yp2nr3.
Pełny tekst źródła國立交通大學
統計學研究所
107
There always have a trade-off: Performance or Interpretability. The complex model, such as ensemble learning can achieve outstanding prediction accuracy. However, it is not easy to interpret the complex model. Understanding why a model made a prediction help us to trust the black-box model, and also help users to make decisions. This work plans to use the techniques of explainable machine learning to develop the appropriate model for empirical data with high prediction and good interpretability. In this study, we use the data provided by Taipei City Center for Prevention of Domestic Violence and Sexual Assault (臺北市家庭暴力暨性侵害防治中心) to develop the risk prediction model to predict the recurrence probability of violence accident for the same case before this case is resolved. This prediction model can also provide individual feature explanation and the counterfactual explanation to help social workers conduct an intervention for violence prevention.
Książki na temat "Counterfactual explanations"
Reutlinger, Alexander. Extending the Counterfactual Theory of Explanation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198777946.003.0005.
Pełny tekst źródłaGerstenberg, Tobias, i Joshua B. Tenenbaum. Intuitive Theories. Redaktor Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.28.
Pełny tekst źródłaKutach, Douglas. The Asymmetry of Influence. Redaktor Craig Callender. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199298204.003.0009.
Pełny tekst źródłaFrench, Steven, i Juha Saatsi. Symmetries and Explanatory Dependencies in Physics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198777946.003.0010.
Pełny tekst źródłaSolstad, Torgrim, i Oliver Bott. Causality and Causal Reasoning in Natural Language. Redaktor Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.32.
Pełny tekst źródłaBrady, Henry E. Causation and Explanation in Social Science. Redaktorzy Janet M. Box-Steffensmeier, Henry E. Brady i David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0010.
Pełny tekst źródłaMcGregor, Rafe. A Criminology Of Narrative Fiction. Policy Press, 2021. http://dx.doi.org/10.1332/policypress/9781529208054.001.0001.
Pełny tekst źródłaSilberstein, Michael, W. M. Stuckey i Timothy McDevitt. Relational Blockworld and Quantum Mechanics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198807087.003.0005.
Pełny tekst źródłaSt John, Taylor. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789918.003.0001.
Pełny tekst źródłaStock, Kathleen. Fiction, Belief, and ‘Imaginative Resistance’. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198798347.003.0005.
Pełny tekst źródłaCzęści książek na temat "Counterfactual explanations"
Dandl, Susanne, Christoph Molnar, Martin Binder i Bernd Bischl. "Multi-Objective Counterfactual Explanations". W Parallel Problem Solving from Nature – PPSN XVI, 448–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58112-1_31.
Pełny tekst źródłaKuratomi, Alejandro, Ioanna Miliou, Zed Lee, Tony Lindgren i Panagiotis Papapetrou. "JUICE: JUstIfied Counterfactual Explanations". W Discovery Science, 493–508. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18840-4_35.
Pełny tekst źródłaGuyomard, Victor, Françoise Fessant, Thomas Guyet, Tassadit Bouadi i Alexandre Termier. "Generating Robust Counterfactual Explanations". W Machine Learning and Knowledge Discovery in Databases: Research Track, 394–409. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43418-1_24.
Pełny tekst źródłaGerber, Doris. "Counterfactual Causality and Historical Explanations". W Explanation in Action Theory and Historiography, 167–78. 1 [edition]. | New York : Taylor & Francis, 2019. | Series: Routledge studies in contemporary philosophy ; 121: Routledge, 2019. http://dx.doi.org/10.4324/9780429506048-9.
Pełny tekst źródłaMishra, Pradeepta. "Counterfactual Explanations for XAI Models". W Practical Explainable AI Using Python, 265–78. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7158-2_10.
Pełny tekst źródłaJeanneret, Guillaume, Loïc Simon i Frédéric Jurie. "Diffusion Models for Counterfactual Explanations". W Computer Vision – ACCV 2022, 219–37. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26293-7_14.
Pełny tekst źródłaBoumazouza, Ryma, Fahima Cheikh-Alili, Bertrand Mazure i Karim Tabia. "A Symbolic Approach for Counterfactual Explanations". W Lecture Notes in Computer Science, 270–77. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58449-8_21.
Pełny tekst źródłaJacob, Paul, Éloi Zablocki, Hédi Ben-Younes, Mickaël Chen, Patrick Pérez i Matthieu Cord. "STEEX: Steering Counterfactual Explanations with Semantics". W Lecture Notes in Computer Science, 387–403. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19775-8_23.
Pełny tekst źródłaVan Looveren, Arnaud, i Janis Klaise. "Interpretable Counterfactual Explanations Guided by Prototypes". W Machine Learning and Knowledge Discovery in Databases. Research Track, 650–65. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86520-7_40.
Pełny tekst źródłaLi, Peiyu, Soukaïna Filali Boubrahimi i Shah Muhammad Hamdi. "Motif-Guided Time Series Counterfactual Explanations". W Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, 203–15. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37731-0_16.
Pełny tekst źródłaStreszczenia konferencji na temat "Counterfactual explanations"
Leofante, Francesco, Elena Botoeva i Vineet Rajani. "Counterfactual Explanations and Model Multiplicity: a Relational Verification View". W 20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/kr.2023/78.
Pełny tekst źródłaZhao, Wenqi, Satoshi Oyama i Masahito Kurihara. "Generating Natural Counterfactual Visual Explanations". W Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/742.
Pełny tekst źródłaAlbini, Emanuele, Jason Long, Danial Dervovic i Daniele Magazzeni. "Counterfactual Shapley Additive Explanations". W FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3531146.3533168.
Pełny tekst źródłaJeanneret, Guillaume, Loïc Simon i Frédéric Jurie. "Adversarial Counterfactual Visual Explanations". W 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01576.
Pełny tekst źródłaDervakos, Edmund, Konstantinos Thomas, Giorgos Filandrianos i Giorgos Stamou. "Choose your Data Wisely: A Framework for Semantic Counterfactuals". W Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/43.
Pełny tekst źródłaAryal, Saugat, i Mark T. Keane. "Even If Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI". W Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/732.
Pełny tekst źródłaRodriguez, Pau, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam Laradji, Laurent Charlin i David Vazquez. "Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations". W 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00109.
Pełny tekst źródłaTran, Khanh Hiep, Azin Ghazimatin i Rishiraj Saha Roy. "Counterfactual Explanations for Neural Recommenders". W SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3404835.3463005.
Pełny tekst źródłaZemni, Mehdi, Mickaël Chen, Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez i Matthieu Cord. "OCTET: Object-aware Counterfactual Explanations". W 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01446.
Pełny tekst źródłaArtelt, Andre, Valerie Vaquet, Riza Velioglu, Fabian Hinder, Johannes Brinkrolf, Malte Schilling i Barbara Hammer. "Evaluating Robustness of Counterfactual Explanations". W 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2021. http://dx.doi.org/10.1109/ssci50451.2021.9660058.
Pełny tekst źródłaRaporty organizacyjne na temat "Counterfactual explanations"
Gandelman, Néstor. A Comparison of Saving Rates: Micro Evidence from Seventeen Latin American and Caribbean Countries. Inter-American Development Bank, lipiec 2015. http://dx.doi.org/10.18235/0011701.
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