{"type":"ScholarlyArticle","title":"Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems","authors":[{"name":"Xuezhi Wang","slug":"xuezhi-wang","given":"Xuezhi","family":"Wang","affiliation":""},{"name":"Junier B. Oliva","slug":"junier-b-oliva","given":"Junier B.","family":"Oliva","affiliation":""},{"name":"Jeff G. Schneider","slug":"jeff-g-schneider","given":"Jeff G.","family":"Schneider","affiliation":""},{"name":"Barnabás Póczos","slug":"barnabas-poczos","given":"Barnabás","family":"Póczos","affiliation":""}],"abstract":"Multi-task learning attempts to simultaneously leverage data from multiple domains in order to estimate related functions on each domain. For example, a special case of multi-task learning, transfer learning, is often employed when one has a good estimate of a function on a source domain, but is unable to estimate a related function well on a target domain using only target data. Multi-task/transfer learning problems are usually solved by imposing some kind of smooth relationship among/between tasks. In this paper, we study how different smoothness assumptions on task relations affect the upper bounds of algorithms proposed for these problems under different settings. For general multi-task learning, we study a family of algorithms which utilize a reweighting matrix on task weights to capture the smooth relationship among tasks, which has many instantiations in existing literature. Furthermore, for multi-task learning in a transfer learning framework, we study the recently proposed algorithms for the model shift, where the conditional distribution $P(Y|X)$ is allowed to change across tasks but the change is assumed to be smooth. In addition, we illustrate our results with experiments on both simulated and real data.","doi":null,"url":"https://ijcai.org/proceedings/2016/306","pdf":"https://ijcai.org/proceedings/2016/0306.pdf","markdown":"https://ijcai.org/proceedings/2016/306.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/306","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":306,"pages":{"first":2146,"last":2151},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}