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 4 → 2017 12 9
ASJC Scopus subject areas
- コンピュータ ネットワークおよび通信