# Event-Triggered Time-Varying Bayesian Optimization

arXiv: https://arxiv.org/abs/2208.10790 · HTML: https://mlsanity.com/paper/2208.10790/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 63 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 24 of 24)

- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [Non-stationary Stochastic Optimization](https://mlsanity.com/paper/1307.5449/) (2013)
- [Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach](https://mlsanity.com/paper/2102.05406/) (2021)
- [Bayesian optimization for materials design](https://mlsanity.com/paper/1506.01349/) (2015)
- [A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free](https://mlsanity.com/paper/1902.00980/) (2019)
- [Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits](https://mlsanity.com/paper/2002.02518/) (2020)
- [Bayesian Optimization in AlphaGo](https://mlsanity.com/paper/1812.06855/) (2018)
- [Time-Varying Gaussian Process Bandit Optimization](https://mlsanity.com/paper/1601.06650/) (2016)
- [Local policy search with Bayesian optimization](https://mlsanity.com/paper/2106.11899/) (2021)
- [Minimizing Dynamic Regret and Adaptive Regret Simultaneously](https://mlsanity.com/paper/2002.02085/) (2020)
- [Time-Varying Convex Optimization: Time-Structured Algorithms and Applications](https://mlsanity.com/paper/2006.08500/) (2020)
- [Weighted Gaussian Process Bandits for Non-stationary Environments](https://mlsanity.com/paper/2107.02371/) (2021)
- [A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits](https://mlsanity.com/paper/2201.06532/) (2022)
- [Tracking Most Significant Arm Switches in Bandits](https://mlsanity.com/paper/2112.13838/) (2021)
- [Feedback Linearization based on Gaussian Processes with event-triggered Online Learning](https://mlsanity.com/paper/1911.06565/) (2019)
- [No-Regret Algorithms for Time-Varying Bayesian Optimization](https://mlsanity.com/paper/2102.06296/) (2021)
- [Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study](https://mlsanity.com/paper/1812.06325/) (2018)
- [On Controller Tuning with Time-Varying Bayesian Optimization](https://mlsanity.com/paper/2207.11120/) (2022)
- [Safe and Efficient Model-free Adaptive Control via Bayesian Optimization](https://mlsanity.com/paper/2101.07825/) (2021)
- [Event-triggered Learning](https://mlsanity.com/paper/1904.03042/) (2019)
- [Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL](https://mlsanity.com/paper/2106.15883/) (2021)
- [Online Optimization as a Feedback Controller: Stability and Tracking](https://mlsanity.com/paper/1805.09877/) (2018)

## Built on by (most cited first, 7 of 7)

- [This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization](https://mlsanity.com/paper/2405.14540/) (2024)
- [Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel](https://mlsanity.com/paper/2409.18000/) (2024)
- [Distributed Event-Based Learning via ADMM](https://mlsanity.com/paper/2405.10618/) (2024)
- [Time-Varying Bayesian Optimization Without a Metronome](https://mlsanity.com/paper/2501.18963/) (2025)
- [Model-Based Reinforcement Learning for Control under Time-Varying Dynamics](https://mlsanity.com/paper/2604.02260/) (2026)
- [Asymptotic Performance of Time-Varying Bayesian Optimization](https://mlsanity.com/paper/2505.13012/) (2025)
- [Sharper Regret Bounds for Time-Varying Gaussian Process Bandits with Constant Exploration](https://mlsanity.com/paper/2608.18863/) (2026)

## Abstract (arXiv)

We consider the problem of sequentially optimizing a time-varying objective function using time-varying Bayesian optimization (TVBO). Current approaches to TVBO require prior knowledge of a constant rate of change to cope with stale data arising from time variations. However, in practice, the rate of change is usually unknown. We propose an event-triggered algorithm, ET-GP-UCB, that treats the optimization problem as static until it detects changes in the objective function and then resets the dataset. This allows the algorithm to adapt online to realized temporal changes without the need for exact prior knowledge. The event trigger is based on probabilistic uniform error bounds used in Gaussian process regression. We derive regret bounds for adaptive resets without exact prior knowledge of the temporal changes and show in numerical experiments that ET-GP-UCB outperforms competing GP-UCB algorithms on both synthetic and real-world data. The results demonstrate that ET-GP-UCB is readily applicable without extensive hyperparameter tuning.