Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation.
Shinji ItoDaisuke HatanoHanna SumitaAkihiro YabeTakuro FukunagaNaonori KakimuraKen-ichi KawarabayashiPublished in: NIPS (2017)
Keyphrases
- linear regression
- online learning
- least squares
- online convex optimization
- regret bounds
- worst case
- online algorithms
- linear models
- regret minimization
- lower bound
- linear regression model
- theoretical guarantees
- space complexity
- loss function
- expert advice
- dimensionality reduction
- upper confidence bound
- support vector machine