ennbo 0.0.9

Epistemic Nearest Neighbors

Epistemic Nearest Neighbors

A fast, alternative surrogate for Bayesian optimization

ENN estimates a function's value and associated epistemic uncertainty using a K-Nearest Neighbors model. Queries take $O(N lnK)$ time, where $N$ is the number of observations available for KNN lookups. Compare to an exact GP, which takes $O(N^2)$ time. Additionally, measured running times are very small compared to GPs and other alternative surrogates. [1]

Contents

• ENN model, EpistemicNearestNeighbors [1]
• TuRBO-ENN optimizer, class TurboOptimizer has four modes
  • TURBO_ONE - A clone of the TuRBO [2] reference code, reworked to have an ask()/tell() interface.
  • TURBO_ENN - Same as TURBO_ONE, except uses ENN instead of GP and Pareto(mu, se) instead of Thompson sampling.
  • TURBO_ZERO - Same as TURBO_ONE, except randomly-chosen RAASP [3] candidates are picked to be proposals. There is no surrogate.
  • LHD_ONLY - Just creates an LHD design for every ask(). Good for a baseline and for testing.

[1] Sweet, D., & Jadhav, S. A. (2025). Taking the GP Out of the Loop. arXiv preprint arXiv:2506.12818.
https://arxiv.org/abs/2506.12818
[2] Eriksson, D., Pearce, M., Gardner, J. R., Turner, R., & Poloczek, M. (2020). Scalable Global Optimization via Local Bayesian Optimization. Advances in Neural Information Processing Systems, 32.
https://arxiv.org/abs/1910.01739
[3] Rashidi, B., Johnstonbaugh, K., & Gao, C. (2024). Cylindrical Thompson Sampling for High-Dimensional Bayesian Optimization. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (pp. 3502–3510). PMLR.
https://proceedings.mlr.press/v238/rashidi24a.html

Installation

pip install ennbo

Demonstration

demo_enn.ipynb - Shows how to use EpistemicNearestNeighbors to build and query an ENN model.
demo_turbo_enn.ipynb - Shows how to use TurboOptimizer to optimize the Ackley function.