Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization.
Benjamin LethamRoberto CalandraAkshara RaiEytan BakshyPublished in: NeurIPS (2020)
Keyphrases
- high dimensional
- low dimensional
- dimensionality reduction
- high dimensional data
- quadratic programming
- optimization algorithm
- similarity search
- semidefinite
- linear systems
- manifold learning
- efficient optimization
- dimension reduction
- optimization process
- variable selection
- optimization methods
- optimization problems
- feature space
- genetic algorithm
- monte carlo sampling
- highly non linear
- latent space
- bayesian learning
- bayesian inference
- noisy data
- bayesian networks