Leveraging imperfect grouping and partial support sharing in multitask learning = 태스크의 불완전한 그룹화와 서포트 부분 공유를 이용한 멀티 태스크 학습

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We consider the problem of simultaneously learning multiple sparse representations in the high-dimensional setting, exemplified by Multitask Learning (MTL). We propose a new estimator, which we call Dirty Fusion (DF). DF bridges the gap between dirty models, which decompose parameters into common and model-specific sets, and grouping approaches, which assume models belonging to the same group share the same sparsity pattern or have similar parameter values. DF jointly estimates the model parameters together with their potentially “unclean” group structures, and allows for partial support overlap within each group. We formulate DF estimators as an optimization problem, and incorporate automatic debiasing variables into the learning formulation. We demonstrate the impact of the approach using synthetic and real data.
Advisors
Yang, Eunhoresearcher양은호researcher
Description
한국과학기술원 :전산학부,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학부, 2019.2,[iv, 26 p. :]

Keywords

multitask learning▼amultitask group learning▼afusion regularization▼adirty Model; 멀티 태스크 학습▼a멀티 태스크 그룹 학습▼a퓨전 정규화▼a합성 모델

URI
http://hdl.handle.net/10203/267059
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843549&flag=dissertation
Appears in Collection
CS-Theses_Master(석사논문)
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