Efficient Methods for Online Multiclass Logistic Regression
Abstract
Multiclass logistic regression is a fundamental task in machine learning with applications in classification and boosting. Previous work (Foster et a. 2018) has highlighted the importance of improper predictors for achieving ``fast rates'' in the online multiclass logistic regression problem without suffering exponentially from secondary problem parameters, such as the norm of the predictors in the comparison class. While Foster et al. introduced a statistically optimal algorithm, it is in practice computationally intractable due to its run-time complexity being a large polynomial in the time horizon and dimension of input feature vectors. It has remained an open problem if algorithms exist that are both statistically and computationally efficient. Jezequel et al. answered this question for binary logistic regression by introducing AIOLI, an algorithm based on online newton step.
However, their technique fails to generalize to more than two classes and it remains open whether their algorithm can be applied to practical applications of logistic regression, such as bandit multiclass learning or online boosting.
In this paper, we develop a new algorithm, FOLKLORE, for the problem which runs significantly faster than the algorithm of Foster et al. -- the running time per iteration scales quadratically in the dimension -- at the cost of a linear dependence on the norm of the predictors in the regret bound. This yields the first practical algorithm for online multiclass logistic regression, resolving an open problem of Jezequel et al. We resolve the open questions by deriving an extension of AIOLI to the multi-class setting.
Furthermore, we show that our algorithm can be applied to online bandit multiclass prediction and online multiclass boosting, yielding more practical algorithms for both problems compared to the ones in Foster et al. with similar performance guarantees. Finally, we also provide an online-to-batch conversion result for our algorithm.
However, their technique fails to generalize to more than two classes and it remains open whether their algorithm can be applied to practical applications of logistic regression, such as bandit multiclass learning or online boosting.
In this paper, we develop a new algorithm, FOLKLORE, for the problem which runs significantly faster than the algorithm of Foster et al. -- the running time per iteration scales quadratically in the dimension -- at the cost of a linear dependence on the norm of the predictors in the regret bound. This yields the first practical algorithm for online multiclass logistic regression, resolving an open problem of Jezequel et al. We resolve the open questions by deriving an extension of AIOLI to the multi-class setting.
Furthermore, we show that our algorithm can be applied to online bandit multiclass prediction and online multiclass boosting, yielding more practical algorithms for both problems compared to the ones in Foster et al. with similar performance guarantees. Finally, we also provide an online-to-batch conversion result for our algorithm.
Research Areas
