2 citations · 2 across the 10 of their papers we have counts for
4 papers · 1 filter
MOCA-HESP: Meta High-dimensional Bayesian Optimization for Combinatorial and Mixed Spaces via Hyper-ellipsoid Partitioning
Lam Ngo, Huong Ha, Jeffrey Chan +1
High-dimensional Bayesian Optimization (BO) has attracted significant attention in recent research. However, existing methods have mainly focused on optimizing in continuous domain…
BOIDS: High-dimensional Bayesian Optimization via Incumbent-guided Direction Lines and Subspace Embeddings
Lam Ngo, Huong Ha, Jeffrey Chan +1
When it comes to expensive black-box optimization problems, Bayesian Optimization (BO) is a well-known and powerful solution. Many real-world applications involve a large number of…
High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy
Lam Ngo, Huong Ha, Jeffrey Chan +2
Bayesian Optimization (BO) is an effective method for finding the global optimum of expensive black-box functions. However, it is well known that applying BO to high-dimensional op…
Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation
Huong Ha, Vu Nguyen, Hung Tran-The +3
Gaussian process (GP) based Bayesian optimization (BO) is a powerful method for optimizing black-box functions efficiently. The practical performance and theoretical guarantees of…