Literatura académica sobre el tema "Spurious correlations"
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Artículos de revistas sobre el tema "Spurious correlations"
Ben-Zeev, Talia y Jon R. Star. "Spurious Correlations in Mathematical Thinking". Cognition and Instruction 19, n.º 3 (septiembre de 2001): 253–75. http://dx.doi.org/10.1207/s1532690xci1903_1.
Texto completoWard, Andrew. "“Spurious Correlations and Causal Inferences”". Erkenntnis 78, n.º 3 (11 de noviembre de 2012): 699–712. http://dx.doi.org/10.1007/s10670-012-9411-6.
Texto completoJackson, D. A. y K. M. Somers. "The spectre of ?spurious? correlations". Oecologia 86, n.º 1 (marzo de 1991): 147–51. http://dx.doi.org/10.1007/bf00317404.
Texto completoHalperin, Silas. "Spurious correlations—causes and cures". Psychoneuroendocrinology 11, n.º 1 (enero de 1986): 3–13. http://dx.doi.org/10.1016/0306-4530(86)90028-4.
Texto completoLorenzo-Arribas, Altea, Penny S. Reynolds y Chaitra H. Nagaraja. "Suffrage, Statistics, and Spurious Correlations". CHANCE 36, n.º 4 (2 de octubre de 2023): 51–54. http://dx.doi.org/10.1080/09332480.2023.2290956.
Texto completoFan, Jianqing, Qi-Man Shao y Wen-Xin Zhou. "Are discoveries spurious? Distributions of maximum spurious correlations and their applications". Annals of Statistics 46, n.º 3 (junio de 2018): 989–1017. http://dx.doi.org/10.1214/17-aos1575.
Texto completoBerges, John A. "Ratios, regression statistics, and “spurious” correlations". Limnology and Oceanography 42, n.º 5 (julio de 1997): 1006–7. http://dx.doi.org/10.4319/lo.1997.42.5.1006.
Texto completoPollman, Curtis D. y Donald M. Axelrad. "Mercury bioaccumulation factors and spurious correlations". Science of The Total Environment 496 (octubre de 2014): vi—xii. http://dx.doi.org/10.1016/j.scitotenv.2014.07.050.
Texto completoRichman, Jesse T. y Ryan J. Roberts. "Assessing Spurious Correlations in Big Search Data". Forecasting 5, n.º 1 (28 de febrero de 2023): 285–96. http://dx.doi.org/10.3390/forecast5010015.
Texto completoSorjonen, Kimmo, Gustav Nilsonne, Michael Ingre y Bo Melin. "Spurious correlations in research on ability tilt". Personality and Individual Differences 185 (febrero de 2022): 111268. http://dx.doi.org/10.1016/j.paid.2021.111268.
Texto completoTesis sobre el tema "Spurious correlations"
Dediu, Dan. "Non-Spurious Correlations Between Genetic and Linguistic Diversities in the Context of Human Evolution". Thesis, University of Edinburgh, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490090.
Texto completoTan, David Tatwei Banking & Finance Australian School of Business UNSW. "Corporate governance and firm outcomes: causation or spurious correlation?" Awarded By:University of New South Wales. Banking & Finance, 2009. http://handle.unsw.edu.au/1959.4/43371.
Texto completoBose, Tulika. "Transfer learning for abusive language detection". Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0019.
Texto completoThe proliferation of social media, despite its multitude of benefits, has led to the increased spread of abusive language. Such language, being typically hurtful, toxic, or prejudiced against individuals or groups, requires timely detection and moderation by online platforms. Deep learning models for detecting abusive language have displayed great levels of in-corpus performance but underperform substantially outside the training distribution. Moreover, they require a considerable amount of expensive labeled data for training.This strongly encourages the effective transfer of knowledge from the existing annotated abusive language resources that may have different distributions to low-resource corpora. This thesis studies the problem of transfer learning for abusive language detection and explores various solutions to improve knowledge transfer in cross-corpus scenarios.First, we analyze the cross-corpus generalizability of abusive language detection models without accessing the target during training. We investigate if combining topic model representations with contextual representations can improve generalizability. The association of unseen target comments with abusive language topics in the training corpus is shown to provide complementary information for a better cross-corpus transfer.Secondly, we explore Unsupervised Domain Adaptation (UDA), a type of transductive transfer learning, with access to the unlabeled target corpus. Some popular UDA approaches from sentiment classification are analyzed for cross-corpus abusive language detection. We further adapt a BERT model variant to the unlabeled target using the Masked Language Model (MLM) objective. While the latter improves the cross-corpus performance, the other UDA methods perform sub-optimally. Our analysis reveals their limitations and emphasizes the need for effective adaptation methods suited to this task.As our third contribution, we propose two DA approaches using feature attributions, which are post-hoc model explanations. Particularly, the problem of spurious corpus-specific correlations is studied that restrict the generalizability of classifiers for detecting hate speech, a sub-category of abusive language. While the previous approaches rely on a manually curated list of terms, we automatically extract and penalize the terms causing spurious correlations. Our dynamic approaches improve the cross-corpus performanceover previous works both independently and in combination with pre-defined dictionaries.Finally, we consider transferring knowledge from a resource-rich source to a low-resource target with fewer labeled instances, across different online platforms. A novel training strategy is proposed, which allows flexible modeling of the relative proximity of neighbors retrieved from the resource-rich corpus to learn the amount of transfer. We incorporate neighborhood information with Optimal Transport that permits exploitingthe embedding space geometry. By aligning the joint embedding and label distributions of neighbors, substantial improvements are obtained in low-resource hate speech corpora
Libros sobre el tema "Spurious correlations"
Vigen, Tyler. Spurious Correlations. Hachette Books, 2015.
Buscar texto completoSpurious Correlations. Hachette Books, 2015.
Buscar texto completoCapítulos de libros sobre el tema "Spurious correlations"
Evensen, Geir. "Spurious correlations, localization, and inflation". En Data Assimilation, 237–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03711-5_15.
Texto completoHagos, Misgina Tsighe, Kathleen M. Curran y Brian Mac Namee. "Unlearning Spurious Correlations in Chest X-Ray Classification". En Discovery Science, 387–97. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_26.
Texto completoKumar, Amar, Nima Fathi, Raghav Mehta, Brennan Nichyporuk, Jean-Pierre R. Falet, Sotirios Tsaftaris y Tal Arbel. "Debiasing Counterfactuals in the Presence of Spurious Correlations". En Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging, 276–86. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45249-9_27.
Texto completoBykov, Kirill, Laura Kopf y Marina M. C. Höhne. "Finding Spurious Correlations with Function-Semantic Contrast Analysis". En Communications in Computer and Information Science, 549–72. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44067-0_28.
Texto completoWu, Jiaying y Bryan Hooi. "Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks". En Machine Learning and Knowledge Discovery in Databases, 274–90. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26390-3_17.
Texto completoNegri, Fedra. "Correlation Is Not Causation, Yet… Matching and Weighting for Better Counterfactuals". En Texts in Quantitative Political Analysis, 71–98. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-12982-7_4.
Texto completoSun, Susu, Lisa M. Koch y Christian F. Baumgartner. "Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?" En Lecture Notes in Computer Science, 425–34. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43895-0_40.
Texto completoSheather, Simon J. "Spurious Correlation". En International Encyclopedia of Statistical Science, 1374–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_534.
Texto completoElío, Javier, Marina Georgati, Henning S. Hansen y Carsten Keßler. "Migration Studies with a Compositional Data Approach: A Case Study of Population Structure in the Capital Region of Denmark". En Computational Science and Its Applications – ICCSA 2022 Workshops, 576–93. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10545-6_39.
Texto completoLoehle, Craig. "Forest decline: endogenous dynamics, tree defenses, and the elimination of spurious correlation". En Temporal and Spatial Patterns of Vegetation Dynamics, 65–78. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-2275-4_8.
Texto completoActas de conferencias sobre el tema "Spurious correlations"
Thorn Jakobsen, Terne Sasha, Maria Barrett y Anders Søgaard. "Spurious Correlations in Cross-Topic Argument Mining". En Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.starsem-1.25.
Texto completoWang, Zhao y Aron Culotta. "Identifying Spurious Correlations for Robust Text Classification". En Findings of the Association for Computational Linguistics: EMNLP 2020. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.findings-emnlp.308.
Texto completoYaghoobzadeh, Yadollah, Soroush Mehri, Remi Tachet des Combes, T. J. Hazen y Alessandro Sordoni. "Increasing Robustness to Spurious Correlations using Forgettable Examples". En Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.eacl-main.291.
Texto completoRoss, Alexis, Matthew Peters y Ana Marasovic. "Does Self-Rationalization Improve Robustness to Spurious Correlations?" En Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.emnlp-main.501.
Texto completoKim, Jae Myung, A. Sophia Koepke, Cordelia Schmid y Zeynep Akata. "Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval". En 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2023. http://dx.doi.org/10.1109/cvprw59228.2023.00257.
Texto completoDurmus, Esin, Faisal Ladhak y Tatsunori Hashimoto. "Spurious Correlations in Reference-Free Evaluation of Text Generation". En Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-long.102.
Texto completoGu, Jiatao, Yong Wang, Kyunghyun Cho y Victor O. K. Li. "Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations". En Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-1121.
Texto completoMu, Shanlei, Yaliang Li, Wayne Xin Zhao, Jingyuan Wang, Bolin Ding y Ji-Rong Wen. "Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator". En SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3477495.3531934.
Texto completoBansal, Parikshit y Amit Sharma. "Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers". En Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.127.
Texto completoEisenstein, Jacob. "Informativeness and Invariance: Two Perspectives on Spurious Correlations in Natural Language". En Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.naacl-main.321.
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