Thematische Bibliographien / Data distribution shift

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Auswahl der wissenschaftlichen Literatur zum Thema „Data distribution shift“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 31. Juli 2025

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Zeitschriftenartikel zum Thema "Data distribution shift"

Cheng, Luling, Xue Yang, Luliang Tang, et al. "Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data." ISPRS International Journal of Geo-Information 9, no. 4 (2020): 281. http://dx.doi.org/10.3390/ijgi9040281.

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In taxi management, taxi-driver shift behaviors play a key role in arranging the operation of taxis, which affect the balance between the demand and supply of taxis and the parking spaces. At the same time, these behaviors influence the daily travel of citizens. An analysis of the distribution of taxi-driver shifts, therefore, contributes to transportation management. Compared to the previous research using the real shift records, this study focuses on the spatiotemporal analysis of taxi-driver shifts using big trace data. A two-step strategy is proposed to automatically identify taxi-driver s

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Islind, Anna Sigridur, Tomas Lindroth, Johan Lundin, and Gunnar Steineck. "Shift in translations: Data work with patient-generated health data in clinical practice." Health Informatics Journal 25, no. 3 (2019): 577–86. http://dx.doi.org/10.1177/1460458219833097.

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This article reports on how the introduction of patient-generated health data affects the nurses’ and patients’ data work and unpacks how new forms of data collection trigger shifts in the work with data through translation work. The article is based on a 2.5-year case study examining data work of nurses and patients at a cancer rehabilitation clinic at a Swedish Hospital in which patient-generated health data are gathered by patients and then used outside and within clinical practice for decision-making. The article reports on how data are prepared and translated, that is, made useful by the

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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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Tyagi, Dushyant. "Designing an Effective Combined Shewhart-CUSUM Control Scheme with Exponentially Distributed Data." International Journal of Mathematical, Engineering and Management Sciences 4, no. 5 (2019): 1277–86. http://dx.doi.org/10.33889/ijmems.2019.4.5-101.

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In this paper, the Combined Shewhart-CUSUM control scheme has been proposed to monitor the production process when the quality characteristic follows exponential distribution to quickly detect the shift in the process. The simulated values of ARL are determined after the transformation of the data into approximate normal distribution by Nelson transformation method and adding Shewhart control limits to existing CUSUM Control Chart. Scheme parameters (value of k and h) and out of control ARL are calculated at various shift and at various in-control ARL. Parameters are also calculated to detect

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Kuang, Kun, Hengtao Zhang, Runze Wu, Fei Wu, Yueting Zhuang, and Aijun Zhang. "Balance-Subsampled Stable Prediction Across Unknown Test Data." ACM Transactions on Knowledge Discovery from Data 16, no. 3 (2022): 1–21. http://dx.doi.org/10.1145/3477052.

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In data mining and machine learning, it is commonly assumed that training and test data share the same population distribution. However, this assumption is often violated in practice because of the sample selection bias, which might induce the distribution shift from training data to test data. Such a model-agnostic distribution shift usually leads to prediction instability across unknown test data. This article proposes a novel balance-subsampled stable prediction (BSSP) algorithm based on the theory of fractional factorial design. It isolates the clear effect of each predictor from the confo

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Lee, Giwoong, Jiseung Ahn, and Jeongyeol Choe. "HYBOOD: A Hybrid Generative Model for Out-of-Distribution Detection with Corruption Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18101–9. https://doi.org/10.1609/aaai.v39i17.33991.

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We propose HYBOOD, a hybrid out-of-distribution model based on normalizing flow followed by a simple linear classification model. In real-world settings, it is known that data corruption has a strong influence on model degradation; for example image quality like noise, blur and image geometry like translation, scaling, rotation. MNIST-C, CIFAR10-C are the general synthesized datasets to measure model performance and corruption difficulty in terms of covariate and semantic shifts. HYBOOD shows that the separability between in-distribution, covariate shift, and semantic shift can be represented

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Wang, Da, Lin Li, Wei Wei, Qixian Yu, Jianye Hao, and Jiye Liang. "Improving Generalization in Offline Reinforcement Learning via Latent Distribution Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21053–61. https://doi.org/10.1609/aaai.v39i20.35402.

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Dealing with the distribution shift is a significant challenge when building offline reinforcement learning (RL) models that can generalize from a static dataset to out-of-distribution (OOD) scenarios. Previous approaches have employed pessimism or conservatism strategies. More recently, data-driven work has taken a distributional perspective, treating offline data as a domain adaptation problem. However, these methods use heuristic techniques to simulate distribution shifts, resulting in a limited diversity of artificially created distribution gaps. In this paper, we propose a novel perspecti

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Ye, Nanyang, Lin Zhu, Jia Wang, et al. "Certifiable Out-of-Distribution Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10927–35. http://dx.doi.org/10.1609/aaai.v37i9.26295.

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Machine learning methods suffer from test-time performance degeneration when faced with out-of-distribution (OoD) data whose distribution is not necessarily the same as training data distribution. Although a plethora of algorithms have been proposed to mitigate this issue, it has been demonstrated that achieving better performance than ERM simultaneously on different types of distributional shift datasets is challenging for existing approaches. Besides, it is unknown how and to what extent these methods work on any OoD datum without theoretical guarantees. In this paper, we propose a certifiab

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Rezaei, Ashkan, Anqi Liu, Omid Memarrast, and Brian D. Ziebart. "Robust Fairness Under Covariate Shift." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9419–27. http://dx.doi.org/10.1609/aaai.v35i11.17135.

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Making predictions that are fair with regard to protected attributes (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing techniques derive a fair model from sampled labeled data relying on the assumption that training and testing data are identically and independently drawn (iid) from the same distribution. In practice, distribution shift can and does occur between training and testing datasets as the characteristics of individuals interacting with the machine learning system change. We investigate fairness under covariate shift, a relaxation o

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Lone, Showkat Ahmad, Zahid Rasheed, Sadia Anwar, Majid Khan, Syed Masroor Anwar, and Sana Shahab. "Enhanced fault detection models with real-life applications." AIMS Mathematics 8, no. 8 (2023): 19595–636. http://dx.doi.org/10.3934/math.20231000.

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<abstract> <p>Nonconforming events are rare in high-quality processes, and the time between events (TBE) may follow a skewed distribution, such as the gamma distribution. This study proposes one- and two-sided triple homogeneously weighted moving average charts for monitoring TBE data modeled by the gamma distribution. These charts are labeled as the THWMA TBE charts. Monte Carlo simulations are performed to approximate the run length distribution of the one- and two-sided THWMA TBE charts. The THWMA TBE charts are compared to competing charts like the DHWMA TBE, HWMA TBE, THWMA TB

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Dissertationen zum Thema "Data distribution shift"

Dadalto, Câmara Gomes Eduardo. "Improving artificial intelligence reliability through out-of-distribution and misclassification detection." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG018.

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Cette thèse explore l'intersection cruciale entre l'apprentissage automatique (IA) et la sécurité, visant à résoudre les défis liés au déploiement de systèmes intelligents dans des scénarios réels. Malgré des progrès significatifs en IA, des préoccupations liées à la confidentialité, à l'équité et à la fiabilité ont émergé, incitant à renforcer la fiabilité des systèmes d'IA. L'objectif central de la thèse est de permettre aux algorithmes d'IA d'identifier les écarts par rapport au comportement normal, contribuant ainsi à la sécurité globale des systèmes intelligents.La thèse commence par étab

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Lowry, Sonia L. "Analysis of statnamic load test data using a load shed distribution model." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001238.

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Bickel, Steffen. "Learning under differing training and test distributions." Phd thesis, Universität Potsdam, 2008. http://opus.kobv.de/ubp/volltexte/2009/3333/.

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One of the main problems in machine learning is to train a predictive model from training data and to make predictions on test data. Most predictive models are constructed under the assumption that the training data is governed by the exact same distribution which the model will later be exposed to. In practice, control over the data collection process is often imperfect. A typical scenario is when labels are collected by questionnaires and one does not have access to the test population. For example, parts of the test population are underrepresented in the survey, out of reach, or do not retu

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Abecidan, Rony. "Stratégies d'apprentissage robustes pour la détection de manipulation d'images." Electronic Thesis or Diss., Centrale Lille Institut, 2024. http://www.theses.fr/2024CLIL0025.

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Aujourd'hui, la manipulation d'images à des fins non éthiques est courante, notamment sur les réseaux sociaux et dans la publicité. Les utilisateurs malveillants peuvent par exemple créer des images synthétiques convaincantes pour tromper le public ou dissimuler des messages dans des images numériques, posant des risques pour la sécurité nationale. Les chercheurs en analyse forensique d'image travaillent donc avec les forces de l'ordre pour détecter ces manipulations. Les méthodes d'analyse forensique les plus avancées utilisent notamment des réseaux neuronaux convolutifs pour les détecter. Ce

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Neubert, Karin. "Das nichtparametrische Behrens-Fisher-Problem: ein studentisierter Permutationstest und robuste Konfidenzintervalle für den Shift-Effekt." Doctoral thesis, 2006. http://hdl.handle.net/11858/00-1735-0000-000D-F21D-C.

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Bücher zum Thema "Data distribution shift"

Berg, John C. Leave It in the Ground. ABC-CLIO, LLC, 2019. http://dx.doi.org/10.5040/9798400677960.

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Employing scientific explanations and hard data, this book shows why coal is such a problem, how the pro-coal forces got to be so powerful, and how those forces might be defeated through political activism. Coal provided the energy to build modern civilization. This energy source raised standards of living, multiplied the earth's population, and enabled people in developed countries to enjoy leisure time. Today, we know that if we burn all the coal available, climate change will continue to increase. But the use of coal isn't purely an environmental issue; there are also political and economic

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Ray, Ranjan. The Link between Preferences, Prices, Inequality, and Poverty. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198812555.003.0007.

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This paper documents the shift in the literature on prices from being exclusively a macro-topic featuring in the study of inflation, national income accounting, and cross-country income comparisons to one that is firmly rooted in micro-involving economic analysis of household behaviour, welfare, and the distributional implications of changes in relative prices. This paper brings together results from some of the recent studies on Indian National Sample Survey data that examine the effect of price changes on inequality and poverty. It also contains evidence on spatial prices in the context of a

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Ballon, Paola, and Jorge Dávalos. Inequality and the changing nature of work in Peru. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/925-9.

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This paper identifies the socioeconomic drivers of earnings inequality in Peru in the period 2004–18. Using the ENAHO household surveys and data on routine task content of occupations, we apply inequality decomposition methods to the real earnings distribution, its quantiles, and the Gini index. We find that in this period inequality has reduced, with great improvement attributed to reductions in the gender wage gap and macroeconomic factors. However, we did not find strong evidence for factors related to changes in workers’ attributes or shifts in job characteristics, except for a slight enha

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Gaiha, Raghav, Raghbendra Jha, Vani S. Kulkarni, and Nidhi Kaicker. Diets, Nutrition, and Poverty. Edited by Ronald J. Herring. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780195397772.013.029.

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This chapter addresses a persistent tension in current debates over food security, with illustrative data from India. The case allows us to disaggregate concepts in food policy that are often lumped together, so as to better understand what is at stake in rapidly changing economies more generally. Despite rising incomes, there has been sustained decline in per capita nutrient intake in India in recent years. The assertion by Deaton and Dreze (2009) that poverty and undernutrition are unrelated is critically examined. A demand-based model in which food prices and expenditure played significant

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Fleury, James, Bryan Hikari Hartzheim, and Stephen Mamber, eds. The Franchise Era. Edinburgh University Press, 2019. http://dx.doi.org/10.3366/edinburgh/9781474419222.001.0001.

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As Hollywood shifts towards the digital era, the role of the media franchise has become more prominent. Over a series of essays by a range of international scholars, this edited collection argues that the franchise is now an integral element of American media culture. As such, the collection explores the production, distribution, and marketing of franchises as a historical form of media-making. In particular, the essays analyze the complex industrial practice of managing franchises across interconnected online platforms with a global scope, presenting a network of scholarly texts that critical

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Frenneaux, Richard. Music Industry in the Digital Age. Bloomsbury Publishing Plc, 2025. https://doi.org/10.5040/9798765113486.

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The Music Industry in the Digital Age examines at the major shifts brought about by digital technology and platforms in the music industry. Frenneaux looks at how digital production tools, social media, and streaming services have impacted music distribution, creation, and consumption. Along with addressing the challenges of sustaining employment in a fractured attention economy, the book examines the democratization of music production and the ascent of independent artists. It looks at how record companies, producers, and A&R's changing roles in a data-driven environment as well as how al

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Buchteile zum Thema "Data distribution shift"

Oza, Poojan, Hien V. Nguyen, and Vishal M. Patel. "Multiple Class Novelty Detection Under Data Distribution Shift." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58571-6_26.

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Diet, Fabian, Moussa Kassem Sbeyti, and Michelle Karg. "Prediction Accuracy & Reliability: Classification and Object Localization Under Distribution Shift." In Studies in Big Data. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66842-5_9.

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Svensson, Emma, Hannah Rosa Friesacher, Adam Arany, Lewis Mervin, and Ola Engkvist. "Temporal Evaluation of Uncertainty Quantification Under Distribution Shift." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72381-0_11.

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AbstractUncertainty quantification is emerging as a critical tool in high-stakes decision-making processes, where trust in automated predictions that lack accuracy and precision can be time-consuming and costly. In drug discovery, such high-stakes decisions are based on modeling the properties of potential drug compounds on biological assays. So far, existing uncertainty quantification methods have primarily been evaluated using public datasets that lack the temporal context necessary to understand their performance over time. In this work, we address the pressing need for a comprehensive, lar

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Thimonier, Hugo, Fabrice Popineau, Arpad Rimmel, Bich-Liên Doan, and Fabrice Daniel. "Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4581-4_4.

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AbstractThis study explores the application of anomaly detection (AD) methods in imbalanced learning tasks, focusing on fraud detection using real online credit card payment data. We assess the performance of several recent AD methods and compare their effectiveness against standard supervised learning methods. Offering evidence of distribution shift within our dataset, we analyze its impact on the tested models’ performances. Our findings reveal that LightGBM exhibits significantly superior performance across all evaluated metrics but suffers more from distribution shifts than AD methods. Fur

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Ashmore, Rob, and Matthew Hill. "“Boxing Clever”: Practical Techniques for Gaining Insights into Training Data and Monitoring Distribution Shift." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99229-7_33.

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Stade, Dawid, and Martin Manns. "Robotic Assembly Line Balancing with Multimodal Stochastic Processing Times." In Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27933-1_8.

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AbstractIn this paper, a genetic algorithm for the robotic assembly line balancing problem (RALBP) is developed that supports multimodal stochastic processing times and multiple parallel-working robots per workstation. It has the objective to minimize the amount of workstations at a given production rate and probability limit for violating the cycle time (PL). The algorithm is evaluated on the BARTHOLD data set in a range of 1 % to 50 % for PL using an experimentally determined and a normal distribution for the task times. The increase of PL results in a shift of tasks from rear to front stati

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Kozar, Anastasiia, Janis von Bleichert, Sebastian Breß, et al. "Query Processing on Heterogeneous Hardware." In Scalable Data Management for Future Hardware. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74097-8_2.

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Abstract In modern processor design, power efficiency has become the primary constraint, prompting manufacturers to develop processors that balance energy consumption with the growing demand for speed. This shift has initiated an era of heterogeneous multi-core computing, characterized by machines utilizing various processors such as GPUs, MICs, and FPGAs. These processors significantly enhance performance due to their computational capabilities and memory bandwidth, essential for optimizing query processing performance. However, executing database queries efficiently across diverse processors

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Zhang, Yedi, Guangke Chen, Fu Song, Jun Sun, and Jin Song Dong. "Certified Quantization Strategy Synthesis for Neural Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71162-6_18.

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AbstractQuantization plays an important role in deploying neural networks on embedded, real-time systems with limited computing and storage resources (e.g., edge devices). It significantly reduces the model storage cost and improves inference efficiency by using fewer bits to represent the parameters. However, it was recently shown that critical properties may be broken after quantization, such as robustness and backdoor-freeness. In this work, we introduce the first method for synthesizing quantization strategies that verifiably maintain desired properties after quantization, leveraging a key

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Dasu, Tamraparni, Shankar Krishnan, Dongyu Lin, Suresh Venkatasubramanian, and Kevin Yi. "Change (Detection) You Can Believe in: Finding Distributional Shifts in Data Streams." In Advances in Intelligent Data Analysis VIII. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03915-7_3.

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Zhenchenko, Maryna. "Transformation of Public Policy in Ukrainian Book Publishing as a Basis for Resisting Russian Cultural Expansion During and After the War." In Contributions to Security and Defence Studies. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66434-2_12.

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AbstractThis paper examines the transformation of public policy to strengthen Ukrainian book publishing and counter Russian cultural influence. Employing a mixed-methods approach, it combines analysis of legislative changes and industry reports with expert interviews involving key stakeholders such as publishers, editors, and government officials.The findings reveal a discernible trend that has intensified since the Russian invasion in 2014: a decrease in the presence of Russian books in the Ukrainian market and a simultaneous rise in support for Ukrainian publishing. Legislative alterations,

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Konferenzberichte zum Thema "Data distribution shift"

Chen, Sisi, Weijie Liu, Xiaoxi Zhang, Hong Xu, Wanyu Lin, and Xu Chen. "Adaptive Personalized Federated Learning for Non-IID Data with Continual Distribution Shift." In 2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS). IEEE, 2024. http://dx.doi.org/10.1109/iwqos61813.2024.10682851.

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Pang, Junjie, Haohua Du, Zhiyi Liu, Xiaoya Xu, and YuanHao Feng. "FedDCS: Dynamical Client Selection for Federated Learning in Mobile Scenarios with Label Distribution Shift." In 2024 10th International Conference on Big Data Computing and Communications (BigCom). IEEE, 2024. https://doi.org/10.1109/bigcom65357.2024.00023.

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Teng, Yao Long, Htet Naing, and Wentong Cai. "Integrating Data and Rules: A Hybrid Approach for Robust Lane Change Intention Prediction Under Distribution Shift." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10919738.

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Yichuan, Shi, Olivera Kotevska, Viktor Reshniak, and Amir Sadovnik. "Assessing Membership Inference Attacks under Distribution Shifts." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825580.

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Kim, Min-Seon, Ling Liu, and Hyuk-Yoon Kwon. "OL4TeX: Adaptive Online Learning for Text Classification under Distribution Shifts." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10826003.

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Wang, Yuzheng, Dingkang Yang, Zhaoyu Chen, et al. "De-Confounded Data-Free Knowledge Distillation for Handling Distribution Shifts." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01199.

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Zhu, Yichen, Jian Yuan, Bo Jiang, et al. "Prediction with Incomplete Data under Agnostic Mask Distribution Shift." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/525.

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Data with missing values is ubiquitous in many applications. Recent years have witnessed increasing attention on prediction with only incomplete data consisting of observed features and a mask that indicates the missing pattern. Existing methods assume that the training and testing distributions are the same, which may be violated in real-world scenarios. In this paper, we consider prediction with incomplete data in the presence of distribution shift. We focus on the case where the underlying joint distribution of complete features and label is invariant, but the missing pattern, i.e., mask di

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Xie, Hui, Xuanxuan Liu, and Li Guo. "Semi-supervised One-pass Learning under Distribution Shift." In ICBDT 2023: 2023 6th International Conference on Big Data Technologies. ACM, 2023. http://dx.doi.org/10.1145/3627377.3627446.

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Hu, Xuanming, Wei Fan, Kun Yi, et al. "Boosting Urban Prediction via Addressing Spatial-Temporal Distribution Shift." In 2023 IEEE International Conference on Data Mining (ICDM). IEEE, 2023. http://dx.doi.org/10.1109/icdm58522.2023.00025.

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Li, Juren, Yang Yang, Youmin Chen, Jianfeng Zhang, Zeyu Lai, and Lujia Pan. "DWLR: Domain Adaptation under Label Shift for Wearable Sensor." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/489.

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Wearable sensors play a crucial role in real-world scenarios, such as human activity recognition, sleep monitoring and electrocardiogram monitoring. However, deploying classifiers on them is challenged by distribution shifts across users and devices. Unsupervised domain adaptation (UDA) is proposed to address this, yet existing methods mostly focus on feature distribution shift, neglecting the potential misclassification due to label shift. In this paper, we propose Domain adaptation under label shift for Wearable sensor with Learnable Reweighting (DWLR) to handle both feature and label shifts

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Berichte der Organisationen zum Thema "Data distribution shift"

Dubeck, Margaret M., Jonathan M. B. Stern, and Rehemah Nabacwa. Learning to Read in a Local Language in Uganda: Creating Learner Profiles to Track Progress and Guide Instruction Using Early Grade Reading Assessment Results. RTI Press, 2021. http://dx.doi.org/10.3768/rtipress.2021.op.0068.2106.

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The Early Grade Reading Assessment (EGRA) is used to evaluate studies and monitor projects that address reading skills in low- and middle-income countries. Results are often described solely in terms of a passage-reading subtask, thereby overlooking progress in related skills. Using archival data of cohort samples from Uganda at two time points in three languages (Ganda, Lango, and Runyankore-Rukiga), we explored a methodology that uses passage-reading results to create five learner profiles: Nonreader, Beginner, Instructional, Fluent, and Next-Level Ready. We compared learner profiles with re

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Collins, Kimberly, Raffi Der Wartanian, Francisca Beer, and Yunfei Hou. Moving Towards the Electrification of Medium- and Heavy-Duty Vehicles in the Inland Empire. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2305.1.

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This report investigates the transition to zero-emission medium- and heavy-duty vehicles (MDHD) within California’s Inland Empire (IE), emphasizing the significance of electric vehicle charging infrastructure and opportunity charging strategies in facilitating sustainable transportation. Utilizing a mixed-methods approach that combines a systematic literature review, geospatial and big data analytics, and thematic analysis of expert interviews, the study explores the multifaceted challenges and opportunities of electrification. The literature review assesses policies at various governance leve

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Maupin, Julie, and Dr Michael Mamoun. DTPH56-06-T-0004 Plastic Pipe Failure, Risk, and Threat Analysis. Pipeline Research Council International, Inc. (PRCI), 2006. http://dx.doi.org/10.55274/r0012119.

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Reports, publications, papers, and databases were reviewed to better define risks and threats to plastic gas distribution piping. Failure modes were described for plastic PE piping with the most significant being slow crack growth (SCG). Short-term mechanical tests such as tensile, quick burst, melt index, and density tests did not show a correlation with a material's susceptibility to SCG failure. The bend-back test was able to visually identify 1971 low-ductile inner wall materials. PENT test failure times were reported for materials manufactured during the period1972-1985. The PENT test did

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Gómez, Camilo, Carlos Andrés Quicazán-Moreno, and Hernando Vargas-Herrera. Changes in the distribution of new loans by risk category throughout the post-pandemic credit cycle in Colombia. Banco de la República, 2025. https://doi.org/10.32468/be.1313.

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Following the pandemic, the Colombia’s financial system experienced a pronounced credit cycle, with significant real growth in consumer loans followed by a deceleration from late 2022. This paper uses granular loan-level data to analyse how financial intermediaries adjusted the credit risk composition of new loans throughout this cycle. It examines the implications of these shifts for loan supply dynamics and financial conditions. Additionally, the study explores the interaction between credit risk composition and monetary policy transmission during the 2021–24 period. As monetary tightening l

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Aterido, Reyes, Mary Hallward-Driemeier, and Carmen Pagés. Investment Climate and Employment Growth: The Impact of Access to Finance, Corruption and Regulations across Firms. Inter-American Development Bank, 2007. http://dx.doi.org/10.18235/0011259.

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Using firm level data on 70,000 enterprises in 107 countries, this paper finds important effects of access to finance, business regulations, corruption, and to a lesser extent, infrastructure bottlenecks in explaining patterns of job creation at the firm level. The paper focuses on how the impact of the investment climate varies across sizes of firms. The results suggest strong composition effects: A weak business environment shifts downward the size distribution of firms. In the case of finance and business regulations this occurs by reducing the employment growth of all firms, particularly m

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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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Baruah, Bipasha, Ann Kingiri, Daniel Musyoka, et al. Powering Change: The Critical Role of Women and Youth in Sustainable Energy Transformation. Institute of Development Studies, 2025. https://doi.org/10.19088/cedca.2025.001.

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How do we build economic systems that recognise and work within the biophysical limits of our finite planet while simultaneously reducing poverty and inequality? This has become a defining question of our time, and the global transition to clean energy is increasingly considered an important vehicle via which we might address this ‘trilemma.’ Concerns about environmental sustainability and fossil fuel insecurity have encouraged countries around the world to transition to low-carbon energy supplies derived from clean renewables such as solar, hydro, bioenergy, geothermal and wind. Since produci

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Pasupuleti, Murali Krishna. Phase Transitions in High-Dimensional Learning: Understanding the Scaling Limits of Efficient Algorithms. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1125.

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Abstract: High-dimensional learning models exhibit phase transitions, where small changes in model complexity, data size, or optimization dynamics lead to abrupt shifts in generalization, efficiency, and computational feasibility. Understanding these transitions is crucial for scaling modern machine learning algorithms and identifying critical thresholds in optimization and generalization performance. This research explores the role of high-dimensional probability, random matrix theory, and statistical physics in analyzing phase transitions in neural networks, kernel methods, and convex vs. no

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Salavisa, Isabel, Mark Soares, and Sofia Bizarro. A Critical Assessment of Organic Agriculture in Portugal: A reflection on the agro-food system transition. DINÂMIA'CET-Iscte, 2021. http://dx.doi.org/10.15847/dinamiacet-iul.wp.2021.05.

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Over the last few decades, the organic agriculture sector has experienced sustained growth. Globally, as well as in the European Union and Portugal, organic production accounts for just under 10% of total Utilised Agricultural Area (UAA) (FiBL, 2019; Eurostat, 2019; DGADR, 2019; INE, 2019; GPP, 2019). This growth has been seen in terms of production, number of producers, amount of retail sales, imports and exports. This article attempts to build on the multi-level perspective (MLP) of the socio-technical (ST) transitions theory by employing a whole systems analysis (Geels, 2018) of organic agr

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Shapovalova, Daria, Tavis Potts, John Bone, and Keith Bender. Measuring Just Transition : Indicators and scenarios for a Just Transition in Aberdeen and Aberdeenshire. University of Aberdeen, 2023. http://dx.doi.org/10.57064/2164/22364.

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The North East of Scotland is at the forefront of the global energy transition. With the transformation of the UK’s energy sector over coming decades, the lives of communities and workers in the North East will be directly affected as we collectively transition to a Net Zero economy. A Just Transition refers to a fair distribution of the burdens and benefits as society and the economy shifts to a sustainable low-carbon economy. It calls for action on providing decent green jobs, building community wealth, and embedding participation. While it is a well-established concept in the academic liter

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