References

Abadi, Martin, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, et al. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.” arXiv e-Prints, March, arXiv:1603.04467.
Aggarwal, Charu, ed. 2007. Data Streams – Models and Algorithms. Springer-Verlag.
Bartz, Eva, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann, eds. 2022. Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide. Springer.
Bartz-Beielstein, Thomas. 2023. PyTorch Hyperparameter Tuning with SPOT: Comparison with Ray Tuner and Default Hyperparameters on CIFAR10.” https://github.com/sequential-parameter-optimization/spotPython/blob/main/notebooks/14_spot_ray_hpt_torch_cifar10.ipynb.
———. 2024a. “Evaluation and Performance Measurement.” In, edited by Eva Bartz and Thomas Bartz-Beielstein, 47–62. Singapore: Springer Nature Singapore.
———. 2024b. “Hyperparameter Tuning.” In, edited by Eva Bartz and Thomas Bartz-Beielstein, 125–40. Singapore: Springer Nature Singapore.
———. 2024c. “Introduction: From Batch to Online Machine Learning.” In Online Machine Learning: A Practical Guide with Examples in Python, edited by Eva Bartz and Thomas Bartz-Beielstein, 1–11. Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-7007-0_1.
Bartz-Beielstein, Thomas, and Lukas Hans. 2024. “Drift Detection and Handling.” In Online Machine Learning: A Practical Guide with Examples in Python, edited by Eva Bartz and Thomas Bartz-Beielstein, 23–39. Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-7007-0_3.
Bartz-Beielstein, Thomas, and Martin Zaefferer. 2022. “Hyperparameter Tuning Approaches.” In Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide, edited by Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann, 67–114. Springer.
Bifet, Albert. 2010. Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams. Vol. 207. Frontiers in Artificial Intelligence and Applications. IOS Press.
Bifet, Albert, and Ricard Gavaldà. 2007. “Learning from Time-Changing Data with Adaptive Windowing.” In Proceedings of the 2007 SIAM International Conference on Data Mining (SDM), 443–48.
———. 2009. “Adaptive Learning from Evolving Data Streams.” In Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII, 249–60. IDA ’09. Berlin, Heidelberg: Springer-Verlag.
Bifet, Albert, Geoff Holmes, Richard Kirkby, and Bernhard Pfahringer. 2010a. MOA: Massive Online Analysis.” Journal of Machine Learning Research 99: 1601–4.
———. 2010b. “MOA: Massive Online Analysis.” Journal of Machine Learning Research 11: 1601–4.
Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” arXiv e-Prints, October, arXiv:1810.04805.
Domingos, Pedro M., and Geoff Hulten. 2000. “Mining High-Speed Data Streams.” In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, August 20-23, 2000, edited by Raghu Ramakrishnan, Salvatore J. Stolfo, Roberto J. Bayardo, and Ismail Parsa, 71–80. ACM.
Dosovitskiy, Alexey, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, et al. 2020. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.” arXiv e-Prints, October, arXiv:2010.11929.
Dredze, Mark, Tim Oates, and Christine Piatko. 2010. “We’re Not in Kansas Anymore: Detecting Domain Changes in Streams.” In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 585–95.
Forrester, Alexander, András Sóbester, and Andy Keane. 2008. Engineering Design via Surrogate Modelling. Wiley.
Gaber, Mohamed Medhat, Arkady Zaslavsky, and Shonali Krishnaswamy. 2005. “Mining Data Streams: A Review.” SIGMOD Rec. 34: 18–26.
Gama, João, Pedro Medas, Gladys Castillo, and Pedro Rodrigues. 2004. “Learning with Drift Detection.” In Advances in Artificial Intelligence – SBIA 2004, edited by Ana L. C. Bazzan and Sofiane Labidi, 286–95. Berlin, Heidelberg: Springer Berlin Heidelberg.
Gama, João, Raquel Sebastião, and Pedro Pereira Rodrigues. 2013. “On Evaluating Stream Learning Algorithms.” Machine Learning 90 (3): 317–46.
Gramacy, Robert B. 2020. Surrogates. CRC press.
Hoeglinger, Stefan, and Russel Pears. 2007. “Use of Hoeffding Trees in Concept Based Data Stream Mining.” 2007 Third International Conference on Information and Automation for Sustainability, 57–62.
Ikonomovska, Elena. 2012. “Algorithms for Learning Regression Trees and Ensembles on Evolving Data Streams.” PhD thesis, Jozef Stefan International Postgraduate School.
Jain, Sarthak, and Byron C. Wallace. 2019. Attention is not Explanation.” arXiv e-Prints, February, arXiv:1902.10186.
Keller-McNulty, Sallie, ed. 2004. Statistical Analysis of Massive Data Streams: Proceedings of a Workshop. Washington, DC: Committee on Applied; Theoretical Statistics, National Research Council; National Academies Press.
Lippe, Phillip. 2022. UvA Deep Learning Tutorials.”
Liu, Liyuan, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han. 2019. On the Variance of the Adaptive Learning Rate and Beyond.” arXiv e-Prints, August, arXiv:1908.03265.
Manapragada, Chaitanya, Geoffrey I. Webb, and Mahsa Salehi. 2018. “Extremely Fast Decision Tree.” In KDD’ 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, edited by Chih-Jen Lin and Hui Xiong, 1953–62. United States of America: Association for Computing Machinery (ACM). https://doi.org/10.1145/3219819.3220005.
Masud, Mohammad, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani M Thuraisingham. 2011. “Classification and Novel Class Detection in Concept-Drifting Data Streams Under Time Constraints.” IEEE Transactions on Knowledge and Data Engineering 23 (6): 859–74.
Montiel, Jacob, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, et al. 2021. “River: Machine Learning for Streaming Data in Python.”
Mourtada, Jaouad, Stephane Gaiffas, and Erwan Scornet. 2019. AMF: Aggregated Mondrian Forests for Online Learning.” arXiv e-Prints, June, arXiv:1906.10529. https://doi.org/10.48550/arXiv.1906.10529.
Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–30.
Putatunda, Sayan. 2021. Practical Machine Learning for Streaming Data with Python. Springer.
Santner, T J, B J Williams, and W I Notz. 2003. The Design and Analysis of Computer Experiments. Berlin, Heidelberg, New York: Springer.
Street, W. Nick, and YongSeog Kim. 2001. “A Streaming Ensemble Algorithm (SEA) for Large-Scale Classification.” In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 377–82. KDD ’01. New York, NY, USA: Association for Computing Machinery.
Tay, Yi, Mostafa Dehghani, Dara Bahri, and Donald Metzler. 2020. Efficient Transformers: A Survey.” arXiv e-Prints, September, arXiv:2009.06732.
Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need.” arXiv e-Prints, June, 1–15.
Wiegreffe, Sarah, and Yuval Pinter. 2019. Attention is not not Explanation.” arXiv e-Prints, August, arXiv:1908.04626.