{"type":"ScholarlyArticle","title":"Towards Utilitarian Online Learning -- A Review of Online Algorithms in Open Feature Space","authors":[{"name":"Yi He","slug":"yi-he","given":"Yi","family":"He","affiliation":"Old Dominion University , USA"},{"name":"Christian Schreckenberger","slug":"christian-schreckenberger","given":"Christian","family":"Schreckenberger","affiliation":"University of Mannheim , Germany"},{"name":"Heiner Stuckenschmidt","slug":"heiner-stuckenschmidt","given":"Heiner","family":"Stuckenschmidt","affiliation":"University of Mannheim , Germany"},{"name":"Xindong Wu","slug":"xindong-wu","given":"Xindong","family":"Wu","affiliation":"Zhejiang Lab , China; Hefei University of Technology , China"}],"abstract":"Human intelligence comes from the capability to describe and make sense of the world surrounding us, often in a lifelong manner. Online Learning (OL) allows a model to simulate this capability, which involves processing data in sequence, making predictions, and learning from predictive errors. However, traditional OL assumes a fixed set of features to describe data, which can be restrictive. In reality, new features may emerge and old features may vanish or become obsolete, leading to an open\r\nfeature space. This dynamism can be caused by more advanced or outdated technology for sensing the world, or it can be a natural process of evolution. This paper reviews recent breakthroughs that strived to enable OL in open feature spaces, referred to as Utilitarian Online Learning (UOL). We taxonomize existing UOL models into three categories, analyze their pros and cons, and discuss their application scenarios. We also benchmark the performance of representative UOL models, highlighting open problems, challenges, and potential future directions of this emerging topic.","doi":"10.24963/ijcai.2023/745","url":"https://ijcai.org/proceedings/2023/745","pdf":"https://ijcai.org/proceedings/2023/0745.pdf","markdown":"https://ijcai.org/proceedings/2023/745.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/745","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Survey Track","track":"Survey Track","number":745,"pages":{"first":6647,"last":6655},"keywords":["Survey: Game Theory and Economic Paradigms","Survey: Uncertainty in AI","Survey: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal, 2007] Charu C Aggarwal. 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