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  • Derivative tests
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  • Docs - ai/sherpa), [Paper](https://arxiv.org/pdf/2005.04048.pdf))
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  • On the choice of the low-dimensional domain for globaloptimization via random embedding
  • An Alternative View: When Does SGD Escape Local Minima?
  • Adam: A Method for Stochastic Optimization
  • TAdam: A Robust Stochastic Gradient Optimizer
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  • LaProp: a Better Way to Combine Momentum with Adaptive Gradient
  • A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
  • Practical Bayesian Optimization of Machine Learning Algorithms
  • Taking the Human Out of the Loop: A Review of Bayesian Optimization
  • Bayesian Optimization in High Dimensions via Random Embeddings
  • A Framework for Bayesian Optimization in Embedded Subspaces
  • High Dimensional Bayesian Optimization via Supervised Dimension Reduction
  • Semi-supervised Embedding Learning for High-dimensionalBayesian Optimization
  • BOHB: Robust and Efficient Hyperparameter Optimization at Scale
  • Population Based Training of Neural Networks
  • Approximate Solution of Certain Types of Constrained Optimization Problems
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  • Particle Swarm Optimization
  • Some Aspects of the Sequential Design of Experiments
  • Multi-armed Bandit Algorithmsand Empirical Evaluation
  • A modern Bayesian look at the multi-armed bandit
  • Pareto set
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