2 citations · 2 across the 5 of their papers we have counts for
5 papers
MOBO-OSD: Batch Multi-Objective Bayesian Optimization via Orthogonal Search Directions
Lam Ngo, Huong Ha, Jeffrey Chan +1
Bayesian Optimization (BO) is a powerful tool for optimizing expensive black-box objective functions. While extensive research has been conducted on the single-objective optimizati…
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…
DeeP-Mod: Deep Dynamic Programming based Environment Modelling using Feature Extraction
Chris Child, Lam Ngo
The DeeP-Mod framework builds an environment model using features from a Deep Dynamic Programming Network (DDPN), trained via a Deep Q-Network (DQN). While Deep Q-Learning is effec…
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…