LEARNING NO-REGRET SPARSE GENERALIZED LINEAR MODELS WITH VARYING OBSERVATION(S)

https://m2.mtmt.hu/ hun 2026-10-25 16:11 BookChapter 35718101 APPROVED true Cornell University, United States Western University, Canada Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates Conference code: 200372 Export Date: 27 January 2025 Correspondence Address: Gu, B.; Mohamed bin Zayed University of Artificial IntelligenceUnited Arab Emirates; email: bin.gu@mbzuai.ac.ae 0 false 2026-01-23T14:02:43.016+0000 2025-01-27T12:21:57.814+0000 2025-01-27T12:20:13.699+0000 true 10095290 Kubicsek Ferenc /api/admin/10095290 Admin Kubicsek Ferenc (BME GPK HDR admin5) true 2026-01-23T14:01:39.221+0000 2026-01-23T14:01:39.221+0000 false false true 31 31 /api/publicationtype/31 PublicationType Egyéb konferenciaközlemény 4 true 31 BookChapter true 10000197 Conference paper true 31 31 /api/publicationtype/31 PublicationType Egyéb konferenciaközlemény 4 true 31 BookChapter /api/subtype/10000197 Konferenciaközlemény SubType Konferenciaközlemény (Egyéb konferenciaközlemény) 452 true 10000197 true 1 /api/category/1 Category Tudományos true 1 Li, D. LEARNING NO-REGRET SPARSE GENERALIZED LINEAR MODELS WITH VARYING OBSERVATION(S) 200372 200372 2024 Generalized Linear Models (GLMs) encompass a wide array of regression and classification models, where prediction is a function of a linear combination of the input variables. Often in real-world scenarios, a number of observations would be added into or removed from the existing training dataset, necessitating the development of learning systems that can efficiently train optimal models with varying observations in an online (sequential) manner instead of retraining from scratch. Despite the significance of data-varying scenarios, most existing approaches to sparse GLMs concentrate on offline batch updates, leaving online solutions largely underexplored. In this work, we present the first algorithm without compromising accuracy for GLMs regularized by sparsity-enforcing penalties trained on varying observations. Our methodology is capable of handling the addition and deletion of observations simultaneously, while adaptively updating data-dependent regularization parameters to ensure the best statistical performance. Specifically, we recast sparse GLMs as a bilevel optimization objective upon varying observations and characterize it as an explicit gradient flow in the underlying space for the inner and outer subproblems we are optimizing over, respectively. We further derive a set of rules to ensure a proper transition at regions of non-smoothness, and establish the guarantees of theoretical consistency and finite convergence. Encouraging results are exhibited on real-world benchmarks. © 2024 12th International Conference on Learning Representations, ICLR 2024. 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(2024) Megjelent: 12th International Conference on Learning Representations, ICLR 2024<div class="BookChapter Publication long-list"> <div class="authors"> <img title="Idézőközlemény" style="float: left" src="/frontend/resources/grid/publication-citation-icon.png"> <div class="autype autype0"> <span class="author-name" >Li D. </span> ;    <span class="author-name" >Ling C.X. </span> ;    <span class="author-name" >Xu Z. </span> ;    <span class="author-name" >Xiong H. </span> ;    <span class="author-name" >Gu B. ✉ </span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;35718101" target="_blank">LEARNING NO-REGRET SPARSE GENERALIZED LINEAR MODELS WITH VARYING OBSERVATION(S)</a></div> <div class="InBook"><div class="chapter-in">In:</div> <div class="authors"> </div> <div class="booktitle"><a href="/gui2/?mode=browse&params=publication;35178194" target="_blank">12th International Conference on Learning Representations, ICLR 2024 </a></div> <div class="conference"> Konferencia helye, ideje: <span class="conference-date">2024.05.07. - 2024.05.11.</span> </div> : <span class="publishers">International Conference on Learning Representations, ICLR</span>, <span class="page"> Paper 200372. </span> <span class="year">(2024)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="85200596281" target="_blank" href="http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85200596281"> Scopus </a> </span> </span> <span class="bookchapter-ids">Befoglaló link(ek):</span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="https://openreview.net/group?id=ICLR.cc/2024/Conference#tab-accept-oral" target="_blank" href="https://openreview.net/group?id=ICLR.cc/2024/Conference#tab-accept-oral"> Egyéb URL </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="https://iclr.cc/Conferences/2024" target="_blank" href="https://iclr.cc/Conferences/2024"> Egyéb URL </a> </span> </span> <div class="publication-citation"> <a target="_blank" href="/api/publication?cond=citations.related;eq;35718101&sort=publishedYear,desc&sort=title"> Idézett közlemények száma: 1 </a> </div> <div class="mtid"><span class="long-pub-mtid">Közlemény: 35718101</span> | <span class="status-data status-APPROVED"> Nyilvános </span> <span class="long-book-mtid">Befoglaló: 35178194</span> Idéző | <span class="type-subtype">Egyéb konferenciaközlemény ( Konferenciaközlemény ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">Scopus</span> </div> <div class="notDuplums"><a target="_blank" href="/api/publication/35718101/notduplums">3 Nem duplumnak jelölés</a> </div> <div class="lastModified">Utolsó módosítás: 2025.01.27. 13:21 Kubicsek Ferenc (BME GPK HDR admin5) </div> <pre class="comment" style="margin-top: 0; margin-bottom: 0;"><u>Megjegyzés</u>: Cornell University, United States Western University, Canada Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates Conference code: 200372 Export Date: 27 January 2025 Correspondence Address: Gu, B.; Mohamed bin Zayed University of Artificial Intelligen...</pre> </div> </div>