Efficient sublinear-regret algorithms for online sparse linear regression with limited observation

Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken Ichi Kawarabayashi

研究成果: Conference article査読

1 被引用数 (Scopus)


Online sparse linear regression is the task of applying linear regression analysis to examples arriving sequentially subject to a resource constraint that a limited number of features of examples can be observed. Despite its importance in many practical applications, it has been recently shown that there is no polynomialtime sublinear-regret algorithm unless NP ⊆ BPP, and only an exponential-time sublinear-regret algorithm has been found. In this paper, we introduce mild assumptions to solve the problem. Under these assumptions, we present polynomialtime sublinear-regret algorithms for the online sparse linear regression. In addition, thorough experiments with publicly available data demonstrate that our algorithms outperform other known algorithms.

ジャーナルAdvances in Neural Information Processing Systems
出版ステータスPublished - 2017
イベント31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States
継続期間: 2017 12 42017 12 9

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 信号処理


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