Relevant bibliographies by topics / Dataset shift

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dataset shift'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Dataset shift"

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Sharet, Nir, and Ilan Shimshoni. "Analyzing Data Changes using Mean Shift Clustering." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 07 (2016): 1650016. http://dx.doi.org/10.1142/s0218001416500166.

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A nonparametric unsupervised method for analyzing changes in complex datasets is proposed. It is based on the mean shift clustering algorithm. Mean shift is used to cluster the old and new datasets and compare the results in a nonparametric manner. Each point from the new dataset naturally belongs to a cluster of points from its dataset. The method is also able to find to which cluster the point belongs in the old dataset and use this information to report qualitative differences between that dataset and the new one. Changes in local cluster distribution are also reported. The report can then
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Adams, Niall. "Dataset Shift in Machine Learning." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 1 (2010): 274. http://dx.doi.org/10.1111/j.1467-985x.2009.00624_10.x.

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Guo, Lin Lawrence, Stephen R. Pfohl, Jason Fries, et al. "Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine." Applied Clinical Informatics 12, no. 04 (2021): 808–15. http://dx.doi.org/10.1055/s-0041-1735184.

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Abstract Objective The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts. Methods Studies were included if they were fully published articles that used machine learning and implemented a procedure to mitigate the effects of temporal dataset shift in a clinical setting. We described how dataset shift was m
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He, Zhiqiang. "ECG Heartbeat Classification Under Dataset Shift." Journal of Intelligent Medicine and Healthcare 1, no. 2 (2022): 79–89. http://dx.doi.org/10.32604/jimh.2022.036624.

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Kim, Doyoung, Inwoong Lee, Dohyung Kim, and Sanghoon Lee. "Action Recognition Using Close-Up of Maximum Activation and ETRI-Activity3D LivingLab Dataset." Sensors 21, no. 20 (2021): 6774. http://dx.doi.org/10.3390/s21206774.

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The development of action recognition models has shown great performance on various video datasets. Nevertheless, because there is no rich data on target actions in existing datasets, it is insufficient to perform action recognition applications required by industries. To satisfy this requirement, datasets composed of target actions with high availability have been created, but it is difficult to capture various characteristics in actual environments because video data are generated in a specific environment. In this paper, we introduce a new ETRI-Activity3D-LivingLab dataset, which provides a
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McGaughey, Georgia, W. Patrick Walters, and Brian Goldman. "Understanding covariate shift in model performance." F1000Research 5 (April 7, 2016): 597. http://dx.doi.org/10.12688/f1000research.8317.1.

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Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN’s performance was inferior to either logistic regression or covariate shift. Surprisingly, there was no obvious advantage for using covariate shift to reweight the training data in the examined datasets.
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McGaughey, Georgia, W. Patrick Walters, and Brian Goldman. "Understanding covariate shift in model performance." F1000Research 5 (June 17, 2016): 597. http://dx.doi.org/10.12688/f1000research.8317.2.

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Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN’s performance was inferior to either logistic regression or covariate shift. Surprisingly, there was no obvious advantage for using covariate shift to reweight the training data in the examined datasets.
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McGaughey, Georgia, W. Patrick Walters, and Brian Goldman. "Understanding covariate shift in model performance." F1000Research 5 (October 17, 2016): 597. http://dx.doi.org/10.12688/f1000research.8317.3.

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Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN’s performance was inferior to either logistic regression or covariate shift. Surprisingly, there was no obvious advantage for using covariate shift to reweight the training data in the examined datasets.
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Prasad, Pulicherla Siva, and Senthilrajan Agniraj. "Cross-Domain Adaptation Techniques for Robust Plant Disease Detection: A DANN-CORAL Hybrid Approach." International Journal of Experimental Research and Review 42 (August 30, 2024): 68–84. http://dx.doi.org/10.52756/ijerr.2024.v42.007.

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Plant disease detection with deep learning models has shown promising results, but these models often struggle with generalizing across diverse agricultural environments due to domain shifts in imaging conditions. This paper presents a novel hybrid approach focusing on cross-domain adaptation techniques to address the challenge of domain shift. Our proposed method combines the Domain-Adversarial Neural Network (DANN) with Correlation Alignment (CORAL) to mitigate domain shifts between datasets. The DANN framework enforces domain-invariant feature learning through adversarial training. Using th
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Yu, Jiongchi, Xiaofei Xie, Qiang Hu, et al. "CAShift: Benchmarking Log-Based Cloud Attack Detection under Normality Shift." Proceedings of the ACM on Software Engineering 2, FSE (2025): 1687–709. https://doi.org/10.1145/3729346.

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With the rapid advancement of cloud-native computing, securing cloud environments has become an important task. Log-based Anomaly Detection (LAD) is the most representative technique used in different systems for attack detection and safety guarantee, where multiple LAD methods and relevant datasets have been proposed. However, even though some of these datasets are specifically prepared for cloud systems, they only cover limited cloud behaviors and lack information from a whole-system perspective. Another critical issue to consider is normality shift, which implies that the test distribution
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Dissertations / Theses on the topic "Dataset shift"

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Wang, Fulton. "Addressing two issues in machine learning : interpretability and dataset shift." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122870.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 71-77).<br>In this thesis, I create solutions to two problems. In the first, I address the problem that many machine learning models are not interpretable, by creating a new form of classifier, called the Falling Rule List. This is a decision list classifier where the predicted probabilities are decreasing down the list. Experiments show that the gain in interpretability need not be accompan
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Gogolashvili, Davit. "Global and local Kernel methods for dataset shift, scalable inference and optimization." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS363v2.pdf.

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Dans de nombreux problèmes du monde réel, les données de formation et les données de test ont des distributions différentes. Cette situation est communément appelée " décalage de l'ensemble de données ". Les paramètres les plus courants pour le décalage des ensembles de données souvent considérés dans la littérature sont le décalage des covariables et le décalage des cibles. Dans cette thèse, nous étudions les modèles nonparamétriques appliqués au scénario de changement d'ensemble de données. Nous développons un nouveau cadre pour accélérer la régression par processus gaussien. En particulier,
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Spooner, Amy. "Developing a minimum dataset for nursing team leader handover in the intensive care unit: a prospective interventional study." Thesis, Griffith University, 2018. http://hdl.handle.net/10072/382227.

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Since the World Health Organization listed clinical handover as a top five priority area for patient safety, the evidence-base and resources generated to improve handover communication has increased. But literature specific to the intensive care unit (ICU) handover, particularly handover from shift-to-shift by the ICU nurse Team Leader (TL) remains limited. The aims of this three-phase interventional study focused on understanding current TL handover practices and implementing a handover strategy to improve this practice. The aim of Phase 1 was to determine the content of ICU nursing TL handov
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Fonseca, Eduardo. "Training sound event classifiers using different types of supervision." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/673067.

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The automatic recognition of sound events has gained attention in the past few years, motivated by emerging applications in fields such as healthcare, smart homes, or urban planning. When the work for this thesis started, research on sound event classification was mainly focused on supervised learning using small datasets, often carefully annotated with vocabularies limited to specific domains (e.g., urban or domestic). However, such small datasets do not support training classifiers able to recognize hundreds of sound events occurring in our everyday environment, such as kettle whistles
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Sarr, Jean Michel Amath. "Étude de l’augmentation de données pour la robustesse des réseaux de neurones profonds." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS072.

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Dans cette thèse, nous avons considéré le problème de robustesse des réseaux de neurones. C’est-à-dire que nous avons considéré le cas où le jeu d’apprentissage et le jeu de déploiement ne sont pas indépendamment et identiquement distribués suivant la même source. On appelle cette hypothèse : l’hypothèse i.i.d. Notre principal outil de travail a été l’augmentation de données. En effet, une revue approfondie de la littérature et des expériences préliminaires nous ont montré le potentiel de régularisation de l’augmentation des données. Ainsi, dans un premier temps, nous avons cherché à utiliser
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Vanck, Thomas [Verfasser], Jochen [Akademischer Betreuer] Garcke, Jochen [Gutachter] Garcke, and Reinhold [Gutachter] Schneider. "New importance sampling based algorithms for compensating dataset shifts / Thomas Vanck ; Gutachter: Jochen Garcke, Reinhold Schneider ; Betreuer: Jochen Garcke." Berlin : Technische Universität Berlin, 2016. http://d-nb.info/1156012562/34.

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Luus, Francois Pierre Sarel. "Dataset shift in land-use classification for optical remote sensing." Thesis, 2016. http://hdl.handle.net/2263/56246.

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Multimodal dataset shifts consisting of both concept and covariate shifts are addressed in this study to improve texture-based land-use classification accuracy for optical panchromatic and multispectral remote sensing. Multitemporal and multisensor variances between train and test data are caused by atmospheric, phenological, sensor, illumination and viewing geometry differences, which cause supervised classification inaccuracies. The first dataset shift reduction strategy involves input modification through shadow removal before feature extraction with gray-level co-occurrence matrix and loca
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Books on the topic "Dataset shift"

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Quiñonero-Candela, Joaquin. Dataset shift in machine learning. MIT Press, 2009.

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Quiñonero-Candela, Joaquin, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence, eds. Dataset Shift in Machine Learning. The MIT Press, 2008. http://dx.doi.org/10.7551/mitpress/9780262170055.001.0001.

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Schwaighofer, Anton, Joaquin Quiñonero-Candela, Masashi Sugiyama, and Neil D. Lawrence. Dataset Shift in Machine Learning. MIT Press, 2018.

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Schwaighofer, Anton, Masashi Sugiyama, Neil D. Lawrence, and Joaquin Quinonero-Candela. Dataset Shift in Machine Learning. MIT Press, 2022.

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Ogorzalek, Thomas K. The Cities on the Hill. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190668877.003.0006.

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This largely quantitative chapter zooms out to describe urbanicity in Congress and explore the book’s original dataset of congressional place character to show the downstream effects of the developments in the previous chapters. Several original analyses chronicle the birth of a distinct, national, urban political order and a shift from a “bimodal” Democratic coalition of urban and rural representatives to one in which the relationship between urbanicity and partisanship is monotonic: the more urban a constituency, the more likely it is to be represented by a Democrat. This shift has important
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Croissant, Aurel, David Kuehn, and Tanja Eschenauer-Engler. Dictators' Endgames. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198916673.001.0001.

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Abstract This book examines the political role of the military in “dictators’ endgames”: large-scale nonviolent mass protests in autocracies that demand regime change or the regime leader’s removal from office. It addresses the question why some militaries defend an embattled autocrat by violently cracking down on the protestors, whereas others side with the opposition or decide to stage a coup d’état. The book introduces a systematic definition and operationalization of the “dictator’s endgame” as a situation of non-violent mass mobilization, in which the dictator’s political survival depends
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Steier, Joshua, and Erik Van Hegewald. Understanding the Limits of Artificial Intelligence for Warfighters: Distributional Shift in Cybersecurity Datasets. RAND Corporation, The, 2024.

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Loyle, Cyanne E. Transitional Justice During Armed Conflict. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.218.

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Armed conflict is ultimately about the violent confrontation between two or more groups; however, there is a range of behaviors, both violent and nonviolent, pursued by governments and rebel groups while conflict is ongoing that impacts the course and outcomes of that violence. The use of judicial or quasi-judicial institutions during armed conflict is one such behavior. While there is a well-developed body of literature that examines the conditions under which governments engage with the legacies of violence following armed conflict, we know comparatively little about these same institutions
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Parnell, Tamsin. Constructing Brexit Britain. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9781350436978.

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Combining corpus linguistics, critical discourse analysis, and a discourse analysis of narratives, this book considers one aspect of the Brexit process: the language that journalists, politicians and individuals used to write and talk about what it means to be British and European around the time of Brexit. It reveals a trajectory towards a discourse of national division in Brexit Britain in three datasets: pro-Brexit newspaper articles, UK Government documents, and interviews with individual citizens. Demonstrating the important role that (supra-)national identity discourses played in discuss
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Poplack, Shana. Borrowing. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190256388.001.0001.

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In virtually every bilingual situation empirically studied, borrowed items make up the overwhelming majority of other-language material, but short shrift has been given to this major manifestation of language contact. As a result, scholars have long been divided over whether borrowing is a process distinct from code-switching, leading to long-standing controversy over how best to theorize language mixing strategies. This volume focuses on lexical borrowing as it actually occurs in the discourse of bilingual speakers, building on more than three decades of original research. Based on vast quant
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Book chapters on the topic "Dataset shift"

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da Silva, Camilla, Jed Nisenson, and Jeff Boisvert. "Comparing and Detecting Stationarity and Dataset Shift." In Springer Proceedings in Earth and Environmental Sciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_3.

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AbstractMachine learning algorithms have been increasingly applied to spatial numerical modeling. However, it is important to understand when such methods will underperform. Machine learning algorithms are impacted by dataset shift; when modeling domains of interest present non-stationarities there is no guarantee that the trained models are effective in unsampled areas. This work aims to compare the stationarity requirement of geostatistical methods to the concept of dataset shift. Also, workflow is developed to detect dataset shift in spatial data prior to modeling, this involves applying a
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Qian, Hongyi, Baohui Wang, Ping Ma, Lei Peng, Songfeng Gao, and You Song. "Managing Dataset Shift by Adversarial Validation for Credit Scoring." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20862-1_35.

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Esuli, Andrea, Alessandro Fabris, Alejandro Moreo, and Fabrizio Sebastiani. "The Case for Quantification." In The Information Retrieval Series. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20467-8_1.

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AbstractThis chapter sets the stage for the rest of the book by introducing notions fundamental to quantification, such as class proportions, class distributions and their estimation, dataset shift, and the various subtypes of dataset shift which are relevant to the quantification endeavour. In this chapter we also argue why using classification techniques for estimating class distributions is suboptimal, and we then discuss why learning to quantify has evolved as a task of its own, rather than remaining a by-product of classification.
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Xia, Tong, Jing Han, and Cecilia Mascolo. "Benchmarking Uncertainty Quantification on Biosignal Classification Tasks Under Dataset Shift." In Multimodal AI in Healthcare. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14771-5_25.

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Leyendecker, Lars, Shobhit Agarwal, Thorben Werner, Maximilian Motz, and Robert H. Schmitt. "A Study on Data Augmentation Techniques for Visual Defect Detection in Manufacturing." In Bildverarbeitung in der Automation. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66769-9_6.

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AbstractDeep learning-based defect detection is rapidly gaining importance for automating visual quality control tasks in industrial applications. However, due to usually low rejection rates in manufacturing processes, industrial defect detection datasets are inherent to three severe data challenges: data sparsity, data imbalance, and data shift. Because the acquisition of defect data is highly cost″​=intensive, and Deep Learning (DL) algorithms require a sufficiently large amount of data, we are investigating how to solve these challenges using data oversampling and data augmentation (DA) tec
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Raza, Haider, Girijesh Prasad, and Yuhua Li. "EWMA Based Two-Stage Dataset Shift-Detection in Non-stationary Environments." In IFIP Advances in Information and Communication Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41142-7_63.

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Jin, Qiao, Haoyang Ding, Linfeng Li, Haitao Huang, Lei Wang, and Jun Yan. "Tackling MeSH Indexing Dataset Shift with Time-Aware Concept Embedding Learning." In Database Systems for Advanced Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59419-0_29.

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Jensen, Patrick Møller, Vedrana Andersen Dahl, Rebecca Engberg, Carsten Gundlach, Hans Marin Kjer, and Anders Bjorholm Dahl. "BugNIST a Large Volumetric Dataset for Object Detection Under Domain Shift." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73411-3_2.

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Zhu, Calvin, Michael D. Noseworthy, and Thomas E. Doyle. "Addressing Dataset Shift for Trustworthy Deep Learning Diagnostic Ultrasound Decision Support." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-67868-8_7.

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Peracchio, Lorenzo, Giovanna Nicora, Tommaso Mario Buonocore, Riccardo Bellazzi, and Enea Parimbelli. "Do You Trust Your Model Explanations? An Analysis of XAI Performance Under Dataset Shift." In Artificial Intelligence in Medicine. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66535-6_28.

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Conference papers on the topic "Dataset shift"

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Fingas, Daniel. "Coating Conductance Characterization Using Trenchless Crossing Data." In CONFERENCE 2024. AMPP, 2024. https://doi.org/10.5006/c2024-21037.

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Abstract Coating resistance or conductance values are critical input parameters for the accurate modelling and design of AC mitigation and cathodic protection (CP) systems for pipelines. However, there are relatively few sources for real-world coating resistance values in the literature. To attempt to fill this gap, data from coating quality tests performed on thousands of trenchless pipeline installations was used to characterize the coating resistance of new, primarily fusion bonded epoxy (FBE)-coated pipelines. The tests assess damage incurred as part of the trenchless installations and cov
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Wade, Daniel, Ramon Lugos, Lance Antolick, et al. "Machine Learning Algorithms for HUMS Improvement on Rotorcraft Components." In Vertical Flight Society 71st Annual Forum & Technology Display. The Vertical Flight Society, 2015. http://dx.doi.org/10.4050/f-0071-2015-10196.

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The US Army Condition Based Maintenance program collects data from Health and Usage Monitoring Systems, Flight Data Recorders, Maintenance Records, and Reliability Databases. These data sources are not integrated, but decisions regarding the health of aircraft components are dependent upon the information stored within them. The Army has begun an effort to bring these data sources together using Machine Learning algorithms. Two prototypes will be built using decision-making machines: one for an engine output gearbox and another for a turbo-shaft engine. This paper will discuss the development
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Fu, Rao, Shaoxing Cui, and Xiaoyi Feng. "Mixed Global and Local Attention Alleviates Domain Shift Between Terahertz Image Datasets." In 2024 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2024. https://doi.org/10.1109/icspcc62635.2024.10770373.

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Ushio, Asahi, Francesco Barbieri, Vitor Sousa, Leonardo Neves, and Jose Camacho-Collados. "Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts." In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.aacl-main.25.

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Maggio, Simona, Victor Bouvier, and Leo Dreyfus-Schmidt. "Performance Prediction Under Dataset Shift." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956676.

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Tuia, Devis, Edoardo Pasolli, and William J. Emery. "Dataset shift adaptation with active queries." In 2011 Joint Urban Remote Sensing Event (JURSE). IEEE, 2011. http://dx.doi.org/10.1109/jurse.2011.5764734.

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Spence, David, Christopher Inskip, Novi Quadrianto, and David Weir. "Quantification under class-conditional dataset shift." In ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining. ACM, 2019. http://dx.doi.org/10.1145/3341161.3342948.

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Srey, Ponhvoan, Yuhui Zhang, and Takafumi Kanamori. "Open-World Learning Under Dataset Shift." In 2024 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2024. http://dx.doi.org/10.1109/cai59869.2024.00188.

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Takahashi, Carla C., Luiz C. B. Torres, and Antonio P. Braga. "Gabriel Graph Transductive Approach to Dataset Shift." In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2019. http://dx.doi.org/10.1109/codit.2019.8820327.

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Brugman, Simon, Tomas Sostak, Pradyot Patil, and Max Baak. "popmon: Analysis Package for Dataset Shift Detection." In Python in Science Conference. SciPy, 2022. http://dx.doi.org/10.25080/majora-212e5952-01d.

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Reports on the topic "Dataset shift"

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Johnson, Logan, and Paulina Murray. Continued Long-Term Ecological Monitoring in a Northern Red Oak-White Pine Research Forest Over Five Decades. Forest Ecosystem Monitoring Cooperative, 2024. http://dx.doi.org/10.18125/de7078.

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A red oak-white pine ecosystem on Maine’s coast, Holt Research Forest (HRF), has filled a key niche of long-term ecosystem monitoring since its establishment in 1983. While many landowners in Maine steward oak-pine forests, HRF is a rare research generator on this critical forest type. A recent harvest at HRF in the fall of 2020 provided a timely opportunity to monitor and distribute data on the impact of disturbance on oak-pine ecosystems, particularly in the context of climate change. With funding provided by the Forest Ecosystem Monitoring Fund through Forest Ecosystem Monitoring Cooperativ
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Seema, Seema, Andreas Theocharis, and Andreas Kassler. Evaluate Temporal and Spatio-Temporal Correlations for Different Prosumers Using Solar Power Generation Time Series Dataset. Karlstad University, 2024. http://dx.doi.org/10.59217/yjll7238.

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This study investigates the temporal and spatio-temporal correlations of solar power generation among different prosumers of Uppsala and Halmstad, Sweden. Using solar power generation data from seven prosumer in Uppsala and five in Halmstad, we evaluate the correlation of solar power production generation at specific locations correlates with itself over different time lags (autocorrelation). In addition, we examine the spatiotemporal correlations of solar power production at various locations over a range of lags using time shifted cross correlation. These spatio-temporal correlations can fac
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Kopacki, Lucas, Jennifer Pontius, Anthony D’Amato, and James Duncan. CLIMATE CHANGE EXPOSURE MAPPING FOR NORTHEASTERN TREE SPECIES. Forest Ecosystem Monitoring Cooperative, 2024. http://dx.doi.org/10.18125/24wwx7.

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The uncertainty around the impacts of changing climate poses a significant challenge to sustaining forest ecosystems in the Northeast. Important work has been done downscaling projected changes in climate conditions, modeling shifts in suitable habitat, and mapping disturbance patterns across the region, but no one effort has combined all these predictive tools into one cohesive dataset. The goal of this project is to aggregate these valuable but disparate spatial data sets to quantify a more comprehensive assessment of relative exposure to climate change impacts at the species, and community
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Mascagni, Giulia, and Fabrizio Santoro. The Tax Side of the Pandemic: Compliance Shifts and Funding for Recovery in Rwanda. Institute of Development Studies, 2021. http://dx.doi.org/10.19088/ictd.2021.019.

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While much knowledge is being generated on the impact of the pandemic, we still know very little on its implications on taxation in low-income countries. Yet, tax is crucial to fund crisis response and recovery, in addition to broader development plans and expanded government expenditure. This paper starts addressing this gap using a unique dataset of survey and administrative data from Rwanda. We document two significant shifts in taxpayers’ views: perceptions about the fairness of the tax system improve by 40 per cent, and their attitudes to compliance become more conditional on the provisio
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Pérez Pérez, Jorge, and José G. Nuño-Ledesma. Workers, Workplaces, Sorting, and Wage Dispersion in Mexico. Banco de México, 2024. http://dx.doi.org/10.36095/banxico/di.2024.06.

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Between 2004 and 2018, the spread of wages in Mexico's private labor sector remained stable. Nonetheless, the underlying factors behind salary dispersion underwent significant shifts. To uncover these changes, we analyze an employer-employee dataset comprising the near-universe of Mexico's formal employment. We estimate log wage models and decompose earnings dispersions into worker, workplace and sorting components. At the national level, we find that sorting increased its importance over time. While worker-level factors were the main contributors to salary variability in the 2004-2008 period,
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Clark, Andrew E., Angela Greulich, and Hippolyte d’Albis. The age U-shape in Europe: the protective role of partnership. Verlag der Österreichischen Akademie der Wissenschaften, 2021. http://dx.doi.org/10.1553/populationyearbook2021.res3.1.

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In this study, we ask whether the U-shaped relationship between life satisfactionand age is flatter for individuals who are partnered. An analysis of cross-sectionalEU-SILC data indicates that the decline in life satisfaction from the teens to thefifties is almost four times larger for non-partnered than for partnered individuals,whose life satisfaction essentially follows a slight downward trajectory with age.However, the same analysis applied to three panel datasets (BHPS, SOEP andHILDA) reveals a U-shape for both groups, albeit somewhat flatter for the partneredthan for the non-partnered in
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Chan, Melvin Chee Yeen, and Jennifer Pei-Ling Tan. Secondary quantitative analysis of core research data (2004-2010): A multilevel study of academic achievement and 21st century competencies. National Institute of Education, Nanyang Technological University, Singapore, 2020. https://doi.org/10.32658/10497/22604.

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The Core Research Programme is a large-scale representative study of teaching, learning and cognitive assessment practices and student outcomes. Within this major project, survey and assessment data were collected across three subsidiary projects. Core 1 Panel 2 (2004) and Core 2 Panel 2 (2010) are two unique datasets that focus on how school, classroom and student level factors contribute to individual variation in student achievement and other key 21st century (21C) learning outcomes. Core 1 Panel 6 (2008), on the other hand, is another study that captures a broader range of affective, educa
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Rosenblat, Sruly, Tim O'Reilly, and Ilan Strauss. Beyond Public Access in LLM Pre-Training Data: Non-public book content in OpenAI’s Models. AI Disclosures Project, Social Science Research Council, 2025. https://doi.org/10.35650/aidp.4111.d.2025.

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Using a legally obtained dataset of 34 copyrighted O’Reilly Media books, we apply the DE-COP membership inference attack method to investigate whether OpenAI’s large language models were trained on copyrighted content without consent. Our AUROC scores show that GPT-4o, OpenAI’s more recent and capable model, demonstrates strong recognition of paywalled O’Reilly book content (AUROC = 82%), compared to OpenAI’s earlier model GPT-3.5 Turbo. In contrast, GPT-3.5 Turbo shows greater relative recognition of publicly accessible O’Reilly book samples. GPT-4o Mini, as a much smaller model, shows no kno
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Tait, Emma, Pia Ruisi-Besares, Matthias Sirch, Alyx Belisle, Jennifer Pontius, and Elissa Schuett. Technical Report: Monitoring and Communicating Changes in Disturbance Regimes (Version 1.0). Forest Ecosystem Monitoring Cooperative, 2021. http://dx.doi.org/10.18125/cc0a0l.

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Shifts in disturbance patterns across the Northeast are of increasing concern as the climate continues to change. In particular, changes in patterns of frequency, severity and extent of disturbance event may have detrimental cascading impacts on forest ecosystems and human communities. To explore how changing disturbance regimes might impact future forest health and management it is necessary to understand the historical trends and impacts of disturbance in the region. Although individual types of disturbance have already been analyzed, there is a need for a consolidated overview of the curren
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Calcagno, Juan Carlos, and Mariana Alfonso. Minority Enrollments at Public Universities of Diverse Selectivity Levels under Different Admission Regimes: The Case of Texas. Inter-American Development Bank, 2007. http://dx.doi.org/10.18235/0010878.

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This study describes how minority enrollment probabilities respond to changes in admission policies from affirmative-action to merit-only programs and then to percentage plans when the demographic composition of the potential pool of applicants is also shifting. It takes advantage of admission policy changes that occurred in the state of Texas with the Hopwood and HB588 decisions and of a unique administrative dataset that includes applications, admissions, and enrollments for three public universities of different selectivity levels. The findings suggest that the elimination of affirmative ac
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