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Related Questions
- How do the convergence rates of Thompson sampling and Upper Confidence Bound (UCB) compare in stochastic continuous optimization?
- Can you explain the differences in convergence rates between the greedy and entropy search acquisition functions in discrete optimization?
- How does the convergence rate of the Bayesian optimization algorithm with the probability of improvement acquisition function compare to that with the expected improvement acquisition function in continuous optimization?
- What are the main factors that affect the convergence rate of the acquisition functions in discrete optimization, such as the number of iterations or the budget?
- Can you discuss the trade-offs between exploration and exploitation in the context of convergence rates of different acquisition functions in continuous and discrete optimization?
- How do the convergence rates of the acquisition functions change when the problem complexity increases, such as in high-dimensional or noisy optimization problems?
- Can you compare the convergence rates of the Bayesian optimization algorithm with different acquisition functions, such as the entropy search and the probability of improvement, in continuous and discrete optimization?
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