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Yet, to date, there is no established methodology to answer the key questions:\n            <jats:italic>With which samples to retrain? When should we retrain?<\/jats:italic>\n          <\/jats:p>\n          <jats:p>\n            We address these questions with the sample selection system Memento, which maintains a training set with the \"most useful\" samples to maximize sample space coverage. Memento particularly benefits rare patterns---the notoriously long \"tail\" in networking---and allows assessing rationally\n            <jats:italic>when<\/jats:italic>\n            retraining may help, i.e., when the coverage changes.\n          <\/jats:p>\n          <jats:p>We deployed Memento on Puffer, the live-TV streaming project, and achieved a 14 % reduction of stall time, 3.5\u00d7 the improvement of random sample selection. Memento is model-agnostic and can be applied beyond video streaming.<\/jats:p>","DOI":"10.1145\/3687234.3687237","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T18:29:00Z","timestamp":1722968940000},"page":"10-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["On Sample Selection for Continual Learning: A Video Streaming Case Study"],"prefix":"10.1145","volume":"54","author":[{"given":"Alexander","family":"Dietm\u00fcller","sequence":"first","affiliation":[{"name":"ETH Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Romain","family":"Jacob","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurent","family":"Vanbever","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,6]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Puffer. 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