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Approximate GP model, Multi-Output Risk Measures, Bug Fixes and Performance Improvements
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@Balandat Balandat released this 09 Dec 00:16
v0.6.0
9122d0a

Compatibility

  • Require PyTorch >=1.9 (#1011).
  • Require GPyTorch >=1.6 (#1011).

New Features

  • New ApproximateGPyTorchModel wrapper for various (variational) approximate GP models (#1012).
  • New SingleTaskVariationalGP stochastic variational Gaussian Process model (#1012).
  • Support for Multi-Output Risk Measures (#906, #965).
  • Introduce ModelList and PosteriorList (#829).
  • New Constraint Active Search tutorial (#1010).
  • Add additional multi-objective optimization test problems (#958).

Other Changes

  • Add covar_module as an optional input of MultiTaskGP models (#941).
  • Add min_range argument to Normalize transform to prevent division by zero (#931).
  • Add initialization heuristic for acquisition function optimization that samples around best points (#987).
  • Update initialization heuristic to perturb a subset of the dimensions of the best points if the dimension is > 20 (#988).
  • Modify apply_constraints utility to work with multi-output objectives (#994).
  • Short-cut t_batch_mode_transform decorator on non-tensor inputs (#991).

Performance Improvements

  • Use lazy covariance matrix in BatchedMultiOutputGPyTorchModel.posterior (#976).
  • Fast low-rank Cholesky updates for qNoisyExpectedHypervolumeImprovement (#747, #995, #996).

Bug Fixes

  • Update error handling to new PyTorch linear algebra messages (#940).
  • Avoid test failures on Ampere devices (#944).
  • Fixes to the Griewank test function (#972).
  • Handle empty base_sample_shape in Posterior.rsample (#986).
  • Handle NotPSDError and hitting maxiter in fit_gpytorch_model (#1007).
  • Use TransformedPosterior for subclasses of GPyTorchPosterior (#983).
  • Propagate best_f argument to qProbabilityOfImprovement in input constructors (f5a5f8b)
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