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Статті в журналах з теми "Counterfactual explanations"
Sia, Suzanna, Anton Belyy, Amjad Almahairi, Madian Khabsa, Luke Zettlemoyer, and Lambert Mathias. "Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (June 26, 2023): 9837–45. http://dx.doi.org/10.1609/aaai.v37i8.26174.
Повний текст джерелаAsher, Nicholas, Lucas De Lara, Soumya Paul, and Chris Russell. "Counterfactual Models for Fair and Adequate Explanations." Machine Learning and Knowledge Extraction 4, no. 2 (March 31, 2022): 316–49. http://dx.doi.org/10.3390/make4020014.
Повний текст джерелаVanNostrand, Peter M., Huayi Zhang, Dennis M. Hofmann, and Elke A. Rundensteiner. "FACET: Robust Counterfactual Explanation Analytics." Proceedings of the ACM on Management of Data 1, no. 4 (December 8, 2023): 1–27. http://dx.doi.org/10.1145/3626729.
Повний текст джерелаKenny, Eoin M., and Mark T. Keane. "On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11575–85. http://dx.doi.org/10.1609/aaai.v35i13.17377.
Повний текст джерелаLeofante, Francesco, and Nico Potyka. "Promoting Counterfactual Robustness through Diversity." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (March 24, 2024): 21322–30. http://dx.doi.org/10.1609/aaai.v38i19.30127.
Повний текст джерелаDelaney, Eoin, Arjun Pakrashi, Derek Greene, and 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, no. 20 (March 24, 2024): 22696. http://dx.doi.org/10.1609/aaai.v38i20.30596.
Повний текст джерелаBaron, Sam, Mark Colyvan, and David Ripley. "A Counterfactual Approach to Explanation in Mathematics." Philosophia Mathematica 28, no. 1 (December 2, 2019): 1–34. http://dx.doi.org/10.1093/philmat/nkz023.
Повний текст джерелаSchleich, Maximilian, Zixuan Geng, Yihong Zhang, and Dan Suciu. "GeCo." Proceedings of the VLDB Endowment 14, no. 9 (May 2021): 1681–93. http://dx.doi.org/10.14778/3461535.3461555.
Повний текст джерелаBarzekar, Hosein, and Susan McRoy. "Achievable Minimally-Contrastive Counterfactual Explanations." Machine Learning and Knowledge Extraction 5, no. 3 (August 3, 2023): 922–36. http://dx.doi.org/10.3390/make5030048.
Повний текст джерелаChapman-Rounds, Matt, Umang Bhatt, Erik Pazos, Marc-Andre Schulz, and Konstantinos Georgatzis. "FIMAP: Feature Importance by Minimal Adversarial Perturbation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11433–41. http://dx.doi.org/10.1609/aaai.v35i13.17362.
Повний текст джерелаДисертації з теми "Counterfactual explanations"
Jeyasothy, Adulam. "Génération d'explications post-hoc personnalisées." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS027.
Повний текст джерелаThis 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.
Повний текст джерелаKuo, Chia-Yu, and 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.
Повний текст джерела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.
Книги з теми "Counterfactual explanations"
Reutlinger, Alexander. Extending the Counterfactual Theory of Explanation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198777946.003.0005.
Повний текст джерелаGerstenberg, Tobias, and Joshua B. Tenenbaum. Intuitive Theories. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.28.
Повний текст джерелаKutach, Douglas. The Asymmetry of Influence. Edited by Craig Callender. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199298204.003.0009.
Повний текст джерелаFrench, Steven, and Juha Saatsi. Symmetries and Explanatory Dependencies in Physics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198777946.003.0010.
Повний текст джерелаSolstad, Torgrim, and Oliver Bott. Causality and Causal Reasoning in Natural Language. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.32.
Повний текст джерелаBrady, Henry E. Causation and Explanation in Social Science. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0010.
Повний текст джерелаMcGregor, Rafe. A Criminology Of Narrative Fiction. Policy Press, 2021. http://dx.doi.org/10.1332/policypress/9781529208054.001.0001.
Повний текст джерелаSilberstein, Michael, W. M. Stuckey, and Timothy McDevitt. Relational Blockworld and Quantum Mechanics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198807087.003.0005.
Повний текст джерелаSt John, Taylor. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789918.003.0001.
Повний текст джерелаStock, Kathleen. Fiction, Belief, and ‘Imaginative Resistance’. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198798347.003.0005.
Повний текст джерелаЧастини книг з теми "Counterfactual explanations"
Dandl, Susanne, Christoph Molnar, Martin Binder, and Bernd Bischl. "Multi-Objective Counterfactual Explanations." In 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.
Повний текст джерелаKuratomi, Alejandro, Ioanna Miliou, Zed Lee, Tony Lindgren, and Panagiotis Papapetrou. "JUICE: JUstIfied Counterfactual Explanations." In Discovery Science, 493–508. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18840-4_35.
Повний текст джерелаGuyomard, Victor, Françoise Fessant, Thomas Guyet, Tassadit Bouadi, and Alexandre Termier. "Generating Robust Counterfactual Explanations." In 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.
Повний текст джерелаGerber, Doris. "Counterfactual Causality and Historical Explanations." In 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.
Повний текст джерелаMishra, Pradeepta. "Counterfactual Explanations for XAI Models." In Practical Explainable AI Using Python, 265–78. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7158-2_10.
Повний текст джерелаJeanneret, Guillaume, Loïc Simon, and Frédéric Jurie. "Diffusion Models for Counterfactual Explanations." In Computer Vision – ACCV 2022, 219–37. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26293-7_14.
Повний текст джерелаBoumazouza, Ryma, Fahima Cheikh-Alili, Bertrand Mazure, and Karim Tabia. "A Symbolic Approach for Counterfactual Explanations." In Lecture Notes in Computer Science, 270–77. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58449-8_21.
Повний текст джерелаJacob, Paul, Éloi Zablocki, Hédi Ben-Younes, Mickaël Chen, Patrick Pérez, and Matthieu Cord. "STEEX: Steering Counterfactual Explanations with Semantics." In Lecture Notes in Computer Science, 387–403. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19775-8_23.
Повний текст джерелаVan Looveren, Arnaud, and Janis Klaise. "Interpretable Counterfactual Explanations Guided by Prototypes." In 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.
Повний текст джерелаLi, Peiyu, Soukaïna Filali Boubrahimi, and Shah Muhammad Hamdi. "Motif-Guided Time Series Counterfactual Explanations." In 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.
Повний текст джерелаТези доповідей конференцій з теми "Counterfactual explanations"
Leofante, Francesco, Elena Botoeva, and Vineet Rajani. "Counterfactual Explanations and Model Multiplicity: a Relational Verification View." In 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.
Повний текст джерелаZhao, Wenqi, Satoshi Oyama, and Masahito Kurihara. "Generating Natural Counterfactual Visual Explanations." In 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.
Повний текст джерелаAlbini, Emanuele, Jason Long, Danial Dervovic, and Daniele Magazzeni. "Counterfactual Shapley Additive Explanations." In FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3531146.3533168.
Повний текст джерелаJeanneret, Guillaume, Loïc Simon, and Frédéric Jurie. "Adversarial Counterfactual Visual Explanations." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01576.
Повний текст джерелаDervakos, Edmund, Konstantinos Thomas, Giorgos Filandrianos, and Giorgos Stamou. "Choose your Data Wisely: A Framework for Semantic Counterfactuals." In 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.
Повний текст джерелаAryal, Saugat, and Mark T. Keane. "Even If Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI." In 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.
Повний текст джерелаRodriguez, Pau, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam Laradji, Laurent Charlin, and David Vazquez. "Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00109.
Повний текст джерелаTran, Khanh Hiep, Azin Ghazimatin, and Rishiraj Saha Roy. "Counterfactual Explanations for Neural Recommenders." In 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.
Повний текст джерелаZemni, Mehdi, Mickaël Chen, Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, and Matthieu Cord. "OCTET: Object-aware Counterfactual Explanations." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01446.
Повний текст джерелаArtelt, Andre, Valerie Vaquet, Riza Velioglu, Fabian Hinder, Johannes Brinkrolf, Malte Schilling, and Barbara Hammer. "Evaluating Robustness of Counterfactual Explanations." In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2021. http://dx.doi.org/10.1109/ssci50451.2021.9660058.
Повний текст джерелаЗвіти організацій з теми "Counterfactual explanations"
Gandelman, Néstor. A Comparison of Saving Rates: Micro Evidence from Seventeen Latin American and Caribbean Countries. Inter-American Development Bank, July 2015. http://dx.doi.org/10.18235/0011701.
Повний текст джерела