{"status":"public","title":"Lightweight conditional model extrapolation for streaming data under class-prior shift","quality_controlled":"1","scopus_import":"1","publisher":"Institute of Electrical and Electronics Engineers","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","date_created":"2023-01-12T12:09:38Z","oa_version":"Preprint","oa":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2206.05181"}],"abstract":[{"lang":"eng","text":"We introduce LIMES, a new method for learning with non-stationary streaming data, inspired by the recent success of meta-learning. The main idea is not to attempt to learn a single classifier that would have to work well across all occurring data distributions, nor many separate classifiers, but to exploit a hybrid strategy: we learn a single set of model parameters from which a specific classifier for any specific data distribution is derived via classifier adaptation. Assuming a multiclass classification setting with class-prior shift, the adaptation step can be performed analytically with only the classifier’s bias terms being affected. Another contribution of our work is an extrapolation step that predicts suitable adaptation parameters for future time steps based on the previous data. In combination, we obtain a lightweight procedure for learning from streaming data with varying class distribution that adds no trainable parameters and almost no memory or computational overhead compared to training a single model. Experiments on a set of exemplary tasks using Twitter data show that LIMES achieves higher accuracy than alternative approaches, especially with respect to the relevant real-world metric of lowest within-day accuracy."}],"department":[{"_id":"ChLa"}],"date_published":"2022-11-29T00:00:00Z","publication_status":"published","date_updated":"2023-08-04T09:06:34Z","month":"11","article_processing_charge":"No","day":"29","type":"conference","isi":1,"year":"2022","volume":2022,"intvolume":"      2022","_id":"12161","language":[{"iso":"eng"}],"conference":{"end_date":"2022-08-25","location":"Montreal, Canada","start_date":"2022-08-21","name":"ICPR: International Conference on Pattern Recognition"},"citation":{"ieee":"P. Tomaszewska and C. Lampert, “Lightweight conditional model extrapolation for streaming data under class-prior shift,” in <i>26th International Conference on Pattern Recognition</i>, Montreal, Canada, 2022, vol. 2022, pp. 2128–2134.","mla":"Tomaszewska, Paulina, and Christoph Lampert. “Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift.” <i>26th International Conference on Pattern Recognition</i>, vol. 2022, Institute of Electrical and Electronics Engineers, 2022, pp. 2128–34, doi:<a href=\"https://doi.org/10.1109/icpr56361.2022.9956195\">10.1109/icpr56361.2022.9956195</a>.","short":"P. Tomaszewska, C. Lampert, in:, 26th International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 2128–2134.","ista":"Tomaszewska P, Lampert C. 2022. Lightweight conditional model extrapolation for streaming data under class-prior shift. 26th International Conference on Pattern Recognition. ICPR: International Conference on Pattern Recognition vol. 2022, 2128–2134.","apa":"Tomaszewska, P., &#38; Lampert, C. (2022). Lightweight conditional model extrapolation for streaming data under class-prior shift. In <i>26th International Conference on Pattern Recognition</i> (Vol. 2022, pp. 2128–2134). Montreal, Canada: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/icpr56361.2022.9956195\">https://doi.org/10.1109/icpr56361.2022.9956195</a>","chicago":"Tomaszewska, Paulina, and Christoph Lampert. “Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift.” In <i>26th International Conference on Pattern Recognition</i>, 2022:2128–34. Institute of Electrical and Electronics Engineers, 2022. <a href=\"https://doi.org/10.1109/icpr56361.2022.9956195\">https://doi.org/10.1109/icpr56361.2022.9956195</a>.","ama":"Tomaszewska P, Lampert C. Lightweight conditional model extrapolation for streaming data under class-prior shift. In: <i>26th International Conference on Pattern Recognition</i>. Vol 2022. Institute of Electrical and Electronics Engineers; 2022:2128-2134. doi:<a href=\"https://doi.org/10.1109/icpr56361.2022.9956195\">10.1109/icpr56361.2022.9956195</a>"},"publication_identifier":{"eisbn":["9781665490627"],"eissn":["2831-7475"]},"external_id":{"isi":["000897707602018"],"arxiv":["2206.05181"]},"author":[{"full_name":"Tomaszewska, Paulina","first_name":"Paulina","last_name":"Tomaszewska"},{"orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph","last_name":"Lampert","first_name":"Christoph"}],"publication":"26th International Conference on Pattern Recognition","page":"2128-2134","doi":"10.1109/icpr56361.2022.9956195"}