001. Chapter 1. Introduction to Bayesian optimization
002. Chapter 1. Introducing Bayesian optimization
003. Chapter 1. What will you learn in this book
004. Chapter 1. Summary
005. Part 1. Modeling with Gaussian processes
006. Chapter 2. Gaussian processes as distributions over functions
007. Chapter 2. Modeling correlations with multivariate Gaussian distributions and Bayesian updates
008. Chapter 2. Going from a finite to an infinite Gaussian
009. Chapter 2. Implementing GPs in Python
010. Chapter 2. Exercise
011. Chapter 2. Summary
012. Chapter 3. Customizing a Gaussian process with the mean and covariance functions
013. Chapter 3. Incorporating what you already know into a GP
014. Chapter 3. Defining the functional behavior with the mean function
015. Chapter 3. Defining variability and smoothness with the covariance function
016. Chapter 3. Exercise
017. Chapter 3. Summary
018. Part 2. Making decisions with Bayesian optimization
019. Chapter 4. Refining the best result with improvement-based policies
020. Chapter 4. Finding improvement in BayesOpt
021. Chapter 4. Optimizing the expected value of improvement
022. Chapter 4. Exercises
023. Chapter 4. Summary
024. Chapter 5. Exploring the search space with bandit-style policies
025. Chapter 5. Being optimistic under uncertainty with the Upper Confidence Bound policy
026. Chapter 5. Smart sampling with the Thompson sampling policy
027. Chapter 5. Exercises
028. Chapter 5. Summary
029. Chapter 6. Using information theory with entropy-based policies
030. Chapter 6. Entropy search in BayesOpt
031. Chapter 6. Exercises
032. Chapter 6. Summary
033. Part 3. Extending Bayesian optimization to specialized settings
034. Chapter 7. Maximizing throughput with batch optimization
035. Chapter 7. Computing the improvement and upper confidence bound of a batch of points
036. Chapter 7. Exercise 1 Extending TS to the batch setting via resampling
037. Chapter 7. Computing the value of a batch of points using information theory
038. Chapter 7. Exercise 2 Optimizing airplane designs
039. Chapter 7. Summary
040. Chapter 8. Satisfying extra constraints with constrained optimization
041. Chapter 8. Constraint-aware decision-making in BayesOpt
042. Chapter 8. Exercise 1 Manual computation of constrained EI
043. Chapter 8. Implementing constrained EI with BoTorch
044. Chapter 8. Exercise 2 Constrained optimization of airplane design
045. Chapter 8. Summary
046. Chapter 9. Balancing utility and cost with multifidelity optimization
047. Chapter 9. Multifidelity modeling with GPs
048. Chapter 9. Balancing information and cost in multifidelity optimization
049. Chapter 9. Measuring performance in multifidelity optimization
050. Chapter 9. Exercise 1 Visualizing average performance in multifidelity optimization
051. Chapter 9. Exercise 2 Multifidelity optimization with multiple low-fidelity approximations
052. Chapter 9. Summary
053. Chapter 10. Learning from pairwise comparisons with preference optimization
054. Chapter 10. Formulating a preference optimization problem and formatting pairwise comparison data
055. Chapter 10. Training a preference-based GP
056. Chapter 10. Preference optimization by playing king of the hill
057. Chapter 10. Summary
058. Chapter 11. Optimizing multiple objectives at the same time
059. Chapter 11. Finding the boundary of the most optimal data points
060. Chapter 11. Seeking to improve the optimal data boundary
061. Chapter 11. Exercise Multiobjective optimization of airplane design
062. Chapter 11. Summary
063. Part 4. Special Gaussian process models
064. Chapter 12. Scaling Gaussian processes to large datasets
065. Chapter 12. Automatically choosing representative points from a large dataset
066. Chapter 12. Optimizing better by accounting for the geometry of the loss surface
067. Chapter 12. Exercise
068. Chapter 12. Summary
069. Chapter 13. Combining Gaussian processes with neural networks
070. Chapter 13. Capturing similarity within structured data
071. Chapter 13. Using neural networks to process complex structured data
072. Chapter 13. Summary
