Academic literature on the topic 'Spurious correlations'
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Journal articles on the topic "Spurious correlations":
Ben-Zeev, Talia, and Jon R. Star. "Spurious Correlations in Mathematical Thinking." Cognition and Instruction 19, no. 3 (September 2001): 253–75. http://dx.doi.org/10.1207/s1532690xci1903_1.
Ward, Andrew. "“Spurious Correlations and Causal Inferences”." Erkenntnis 78, no. 3 (November 11, 2012): 699–712. http://dx.doi.org/10.1007/s10670-012-9411-6.
Jackson, D. A., and K. M. Somers. "The spectre of ?spurious? correlations." Oecologia 86, no. 1 (March 1991): 147–51. http://dx.doi.org/10.1007/bf00317404.
Halperin, Silas. "Spurious correlations—causes and cures." Psychoneuroendocrinology 11, no. 1 (January 1986): 3–13. http://dx.doi.org/10.1016/0306-4530(86)90028-4.
Lorenzo-Arribas, Altea, Penny S. Reynolds, and Chaitra H. Nagaraja. "Suffrage, Statistics, and Spurious Correlations." CHANCE 36, no. 4 (October 2, 2023): 51–54. http://dx.doi.org/10.1080/09332480.2023.2290956.
Fan, Jianqing, Qi-Man Shao, and Wen-Xin Zhou. "Are discoveries spurious? Distributions of maximum spurious correlations and their applications." Annals of Statistics 46, no. 3 (June 2018): 989–1017. http://dx.doi.org/10.1214/17-aos1575.
Berges, John A. "Ratios, regression statistics, and “spurious” correlations." Limnology and Oceanography 42, no. 5 (July 1997): 1006–7. http://dx.doi.org/10.4319/lo.1997.42.5.1006.
Pollman, Curtis D., and Donald M. Axelrad. "Mercury bioaccumulation factors and spurious correlations." Science of The Total Environment 496 (October 2014): vi—xii. http://dx.doi.org/10.1016/j.scitotenv.2014.07.050.
Richman, Jesse T., and Ryan J. Roberts. "Assessing Spurious Correlations in Big Search Data." Forecasting 5, no. 1 (February 28, 2023): 285–96. http://dx.doi.org/10.3390/forecast5010015.
Sorjonen, Kimmo, Gustav Nilsonne, Michael Ingre, and Bo Melin. "Spurious correlations in research on ability tilt." Personality and Individual Differences 185 (February 2022): 111268. http://dx.doi.org/10.1016/j.paid.2021.111268.
Dissertations / Theses on the topic "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.
Tan, 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.
Bose, Tulika. "Transfer learning for abusive language detection." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0019.
The 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
Books on the topic "Spurious correlations":
Vigen, Tyler. Spurious Correlations. Hachette Books, 2015.
Vigen, Tyler. Spurious Correlations. Hachette Books, 2015.
Book chapters on the topic "Spurious correlations":
Evensen, Geir. "Spurious correlations, localization, and inflation." In Data Assimilation, 237–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03711-5_15.
Hagos, Misgina Tsighe, Kathleen M. Curran, and Brian Mac Namee. "Unlearning Spurious Correlations in Chest X-Ray Classification." In Discovery Science, 387–97. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_26.
Kumar, Amar, Nima Fathi, Raghav Mehta, Brennan Nichyporuk, Jean-Pierre R. Falet, Sotirios Tsaftaris, and Tal Arbel. "Debiasing Counterfactuals in the Presence of Spurious Correlations." In 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.
Bykov, Kirill, Laura Kopf, and Marina M. C. Höhne. "Finding Spurious Correlations with Function-Semantic Contrast Analysis." In 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.
Wu, Jiaying, and Bryan Hooi. "Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks." In 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.
Negri, Fedra. "Correlation Is Not Causation, Yet… Matching and Weighting for Better Counterfactuals." In Texts in Quantitative Political Analysis, 71–98. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-12982-7_4.
Sun, Susu, Lisa M. Koch, and Christian F. Baumgartner. "Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?" In Lecture Notes in Computer Science, 425–34. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43895-0_40.
Sheather, Simon J. "Spurious Correlation." In 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.
Elío, Javier, Marina Georgati, Henning S. Hansen, and Carsten Keßler. "Migration Studies with a Compositional Data Approach: A Case Study of Population Structure in the Capital Region of Denmark." In 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.
Loehle, Craig. "Forest decline: endogenous dynamics, tree defenses, and the elimination of spurious correlation." In 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.
Conference papers on the topic "Spurious correlations":
Thorn Jakobsen, Terne Sasha, Maria Barrett, and Anders Søgaard. "Spurious Correlations in Cross-Topic Argument Mining." In 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.
Wang, Zhao, and Aron Culotta. "Identifying Spurious Correlations for Robust Text Classification." In 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.
Yaghoobzadeh, Yadollah, Soroush Mehri, Remi Tachet des Combes, T. J. Hazen, and Alessandro Sordoni. "Increasing Robustness to Spurious Correlations using Forgettable Examples." In 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.
Ross, Alexis, Matthew Peters, and Ana Marasovic. "Does Self-Rationalization Improve Robustness to Spurious Correlations?" In 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.
Kim, Jae Myung, A. Sophia Koepke, Cordelia Schmid, and Zeynep Akata. "Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2023. http://dx.doi.org/10.1109/cvprw59228.2023.00257.
Durmus, Esin, Faisal Ladhak, and Tatsunori Hashimoto. "Spurious Correlations in Reference-Free Evaluation of Text Generation." In 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.
Gu, Jiatao, Yong Wang, Kyunghyun Cho, and Victor O. K. Li. "Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations." In 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.
Mu, Shanlei, Yaliang Li, Wayne Xin Zhao, Jingyuan Wang, Bolin Ding, and Ji-Rong Wen. "Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator." In 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.
Bansal, Parikshit, and Amit Sharma. "Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers." In 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.
Eisenstein, Jacob. "Informativeness and Invariance: Two Perspectives on Spurious Correlations in Natural Language." In 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.