A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
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Summary
A tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions using the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function.
- Type
- preprint
- Published
- 2010-12-12
- Cited by
- 2,700
- References
- 108
- Access
- Open access
- OpenAlex
- https://openalex.org/W2099201756
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:1640103
Keywords
Reinforcement learning, Bayesian optimization, Computer science, Bayesian probability, Machine learning
References
- The adaptive decision maker: Name index
- A Non-myopic Utility Function for Statistical Global Optimization Algorithms
- Nonlinear adaptive control using non-parametric Gaussian Process prior models
- Sequential design of computer experiments to minimize integrated response functions
- Automatic Gait Optimization with Gaussian Process Regression
- Efficient Global Optimization of Expensive Black-Box Functions
- Gaussian Processes for Regression and Optimisation
- Stochastic Global Optimization
- Classes of Kernels for Machine Learning: A Statistics Perspective
- Hierarchical control and learning for markov decision processes
- Bayesian Algorithms for One-Dimensional Global Optimization
- Computer experiments and global optimization
- Global optimization : from theory to implementation
- Recent Advances in Hierarchical Reinforcement Learning
- PEGASUS: A policy search method for large MDPs and POMDPs
- A Multi-points Criterion for Deterministic Parallel Global Optimization based on Gaussian Processes
- Prospect Theory. An Analysis of Decision Making Under Risk
- Hedging Strategies for Bayesian Optimization
- Bayesian Gaussian processes for sequential prediction, optimisation and quadrature
- Preference learning with Gaussian processes
Cited by
- Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives
- Adaptive MCMC with Bayesian Optimization
- Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression
- An Efficient Approach for Assessing Hyperparameter Importance
- Learning domain abstractions for long lived robots
- Bayesian Optimization with Inequality Constraints
- Approximate inference in state space models with intractable likelihoods using Gaussian process optimisation
- Gated Bayesian Networks
- Bandit algorithms for searching large spaces
- Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
- Bayesian optimization for learning gaits under uncertainty
- Bayesian policy reuse
- Foundations and Advances in Deep Learning
- An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion
- A Novel Portable Absolute Transient Hot-Wire Instrument for the Measurement of the Thermal Conductivity of Solids
- Bayesian Optimization in a Billion Dimensions via Random Embeddings
- Adaptive Hamiltonian and Riemann manifold Monte Carlo samplers
- Gaussian Process Bandits without Regret: An Experimental Design Approach
- Search space preprocessing in solving complex optimization problems
- Thinking Style and Team Competition Game Performance and Enjoyment
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