# Periodic-GP: Learning Periodic World with Gaussian Process Bandits

arXiv: https://arxiv.org/abs/2105.14422 · HTML: https://mlsanity.com/paper/2105.14422/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 30 May 2021.

## Builds on (most cited first, 8 of 8)

- [Weighted Linear Bandits for Non-Stationary Environments](https://mlsanity.com/paper/1909.09146/) (2019)
- [A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free](https://mlsanity.com/paper/1902.00980/) (2019)
- [Efficient Contextual Bandits in Non-stationary Worlds](https://mlsanity.com/paper/1708.01799/) (2017)
- [Time-Varying Gaussian Process Bandit Optimization](https://mlsanity.com/paper/1601.06650/) (2016)
- [Hedging the Drift: Learning to Optimize under Non-Stationarity](https://mlsanity.com/paper/1903.01461/) (2019)
- [Optimal Exploration-Exploitation in a Multi-Armed-Bandit Problem with Non-stationary Rewards](https://mlsanity.com/paper/1405.3316/) (2014)
- [Simple regret for infinitely many armed bandits](https://mlsanity.com/paper/1505.04627/) (2015)
- [Learning Contextual Bandits in a Non-stationary Environment](https://mlsanity.com/paper/1805.09365/) (2018)

## Built on by (most cited first, 1 of 1)

- [Dynamic Structure Estimation from Bandit Feedback using Nonvanishing Exponential Sums](https://mlsanity.com/paper/2206.00861/) (2022)

## Abstract (arXiv)

We consider the sequential decision optimization on the periodic environment, that occurs in a wide variety of real-world applications when the data involves seasonality, such as the daily demand of drivers in ride-sharing and dynamic traffic patterns in transportation. In this work, we focus on learning the stochastic periodic world by leveraging this seasonal law. To deal with the general action space, we use the bandit based on Gaussian process (GP) as the base model due to its flexibility and generality, and propose the Periodic-GP method with a temporal periodic kernel based on the upper confidence bound. Theoretically, we provide a new regret bound of the proposed method, by explicitly characterizing the periodic kernel in the periodic stationary model. Empirically, the proposed algorithm significantly outperforms the existing methods in both synthetic data experiments and a real data application on Madrid traffic pollution.