Contextual Bayesian Optimization, Input Warping, TuRBO, sampling from polytopes.

Compatibility

• Require PyTorch >=1.7 (#614).
• Require GPyTorch >=1.3 (#614).

New Features

• Models (LCE-A, LCE-M and SAC ) for Contextual Bayesian Optimziation (#581).
  • Implements core models from:
    High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization.
    Q. Feng, B. Letham, H. Mao, E. Bakshy. NeurIPS 2020.
  • See Ax for usage of these models.
• Hit and run sampler for uniform sampling from a polytope (#592).
• Input warping:
  • Core functionality (#607).
  • Kumaraswamy Distribution (#606).
  • Tutorial (8f34871652042219c57b799669a679aab5eed7e3).
• TuRBO-1 tutorial (#598).
  • Implements the method from:
    Scalable Global Optimization via Local Bayesian Optimization.
    D. Eriksson, M. Pearce, J. Gardner, R. D. Turner, M. Poloczek. NeurIPS 2019.

Bug fixes

• Fix bounds of HolderTable synthetic function (#596).
• Fix device issue in MOO tutorial (#621).

Other changes

• Add train_inputs option to qMaxValueEntropy (#593).
• Enable gpytorch settings to override BoTorch defaults for fast_pred_var and debug (#595).
• Rename set_train_data_transform -> preprocess_transform (#575).
• Modify _expand_bounds() shape checks to work with >2-dim bounds (#604).
• Add batch_shape property to models (#588).
• Modify qMultiFidelityKnowledgeGradient.evaluate() to work with project, expand and cost_aware_utility (#594).
• Add list of papers using BoTorch to website docs (#617).