HIGH-DIMENSIONAL BAYESIAN OPTIMIZATION VIA RANDOM PROJECTION OF MANIFOLD SUBSPACES

High-dimensional Bayesian Optimization via Random Projection of Manifold Subspaces (ECML PKDD 2024)

Installation

pip install -r requirements.txt

Geometry-aware synthetic experiment

The geometry-aware synthetic experiments contain in the file geometry_aware_synthetic_exp.py

Example: Running Ackley Sphere with D=500, d=10

python geometry_aware_synthetic_exp.py --test_func Ackley_Sphere_1 --rep 20 --trial_itr 300 --initial_n 10 --high_dim 500 --effective_dim 10 --proj_dim 15 --update_param 3

Geometry-unaware synthetic experiment

The geometry-unaware experiments contain in the file geometry_unaware_synthetic_exp.py

Example: Running Ackley Mix with D=500, d=15

python geometry_unaware_synthetic_exp.py --test_func Ackley_Mix --rep 20 --trial_itr 300 --initial_n 10 --high_dim 500 --effective_dim 15 --proj_dim 15 --update_param 3

LassoBench experiment

The LassoBench experiments contain in the file lasso_exp.py

Example: Running Lasso Hard with D=1000

python lasso_exp.py --test_func Lasso --rep 20 --trial_itr 300 --initial_n 10 --proj_dim 10 --update_param 3

References

This source code is adopted from:

• LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
• High-dimensional Bayesian Optimization via Nested Riemannian Manifolds