Maintenance Release + New Tutorials

Compatibility

• Require GPyTorch >=1.5.1 (#928).

New Features

• Add HigherOrderGP composite Bayesian Optimization tutorial notebook (#864).
• Add Multi-Task Bayesian Optimization tutorial (#867).
• New multi-objective test problems from (#876).
• Add PenalizedMCObjective and L1PenaltyObjective (#913).
• Add a ProximalAcquisitionFunction for regularizing new candidates towards previously generated ones (#919, #924).
• Add a Power outcome transform (#925).

Bug Fixes

• Batch mode fix for HigherOrderGP initialization (#856).
• Improve CategoricalKernel precision (#857).
• Fix an issue with qMultiFidelityKnowledgeGradient.evaluate (#858).
• Fix an issue with transforms with HigherOrderGP. (#889)
• Fix initial candidate generation when parameter constraints are on different device (#897).
• Fix bad in-place op in _generate_unfixed_lin_constraints (#901).
• Fix an input transform bug in fantasize call (#902).
• Fix outcome transform bug in batched_to_model_list (#917).

Other Changes

• Make variance optional for TransformedPosterior.mean (#855).
• Support transforms in DeterministicModel (#869).
• Support batch_shape in RandomFourierFeatures (#877).
• Add a maximize flag to PosteriorMean (#881).
• Ignore categorical dimensions when validating training inputs in MixedSingleTaskGP (#882).
• Refactor HigherOrderGPPosterior for memory efficiency (#883).
• Support negative weights for minimization objectives in get_chebyshev_scalarization (#884).
• Move train_inputs transforms to model.train/eval calls (#894).