Efficient Online Hyperparameter Tuning for Contextual Bandits with Theoretical Guarantees

Core Concepts

The core message of this article is to propose an efficient online continuous hyperparameter tuning framework called Continuous Dynamic Tuning (CDT) for contextual bandits, which can automatically learn the optimal hyperparameter configuration in practice without requiring a pre-defined candidate set. The authors also introduce a novel Zooming TS algorithm with Restarts to handle the non-stationary Lipschitz bandit problem underlying the hyperparameter tuning task, and provide theoretical guarantees on the regret bounds.

Abstract

The article presents an online continuous hyperparameter optimization framework called Continuous Dynamic Tuning (CDT) for contextual bandits. The key contributions are:

Formulating the hyperparameter optimization as a non-stationary Lipschitz continuum-armed bandit problem, where each arm represents a hyperparameter configuration and the corresponding reward is the algorithmic performance.

Proposing the Zooming TS algorithm with Restarts to efficiently solve this Lipschitz bandit problem under the switching environment, which adaptively refines the hyperparameter space and utilizes Thompson Sampling for exploration. A restart technique is introduced to handle the piecewise changes in the bandit environment.

Providing theoretical guarantees on the regret bounds of the proposed CDT framework, showing it can achieve sublinear regret under mild assumptions.

Demonstrating through experiments on both synthetic and real datasets that the CDT framework consistently outperforms existing hyperparameter tuning methods for various contextual bandit algorithms.

The article first reviews the problem setting of contextual bandits and the limitations of existing hyperparameter tuning approaches. It then introduces the Zooming TS algorithm with Restarts for the Lipschitz bandit problem under the switching environment, and integrates it into the CDT framework for online hyperparameter optimization of contextual bandits. Rigorous theoretical analysis and extensive empirical evaluations are provided to validate the effectiveness of the proposed method.

Stats

The article does not contain any explicit numerical data or statistics. The experimental results are presented in the form of cumulative regret curves.

Quotes

None.

Key Insights Distilled From

Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits

by Yue Kang,Cho... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2302.09440.pdf

Deeper Inquiries

How can the proposed CDT framework be extended to handle other types of non-stationarity in the bandit environment beyond the switching environment assumption

The CDT framework can be extended to handle other types of non-stationarity in the bandit environment by adapting the algorithm to different scenarios. For example, instead of assuming a switching environment with piecewise stationary functions, the framework could be modified to accommodate gradual changes in the reward functions over time. This could involve incorporating adaptive learning rates or exploration strategies to adjust to the changing dynamics of the environment. Additionally, the framework could be extended to handle non-stationarity caused by external factors or contextual shifts by incorporating contextual information into the hyperparameter tuning process.

Can the Zooming TS algorithm with Restarts be applied to other continuum-armed bandit problems beyond hyperparameter tuning

The Zooming TS algorithm with Restarts can be applied to other continuum-armed bandit problems beyond hyperparameter tuning by modifying the algorithm to suit the specific characteristics of the problem at hand. For example, the algorithm could be adapted to optimize continuous action selection in reinforcement learning tasks or to tune hyperparameters in other machine learning models. By adjusting the parameters and strategies used in the algorithm, it can be tailored to address a wide range of continuum-armed bandit problems beyond hyperparameter optimization.

What are the potential applications of the online continuous hyperparameter tuning technique beyond contextual bandits, such as in reinforcement learning or other machine learning domains

The online continuous hyperparameter tuning technique proposed in the CDT framework has potential applications beyond contextual bandits in various machine learning domains. One application could be in reinforcement learning, where the technique could be used to dynamically adjust hyperparameters in reinforcement learning algorithms to optimize performance in real-time. Additionally, the technique could be applied to optimize hyperparameters in deep learning models, optimization algorithms, or any machine learning algorithm that requires tuning of parameters. The flexibility and adaptability of the CDT framework make it suitable for a wide range of applications in machine learning beyond contextual bandits.