This release includes many major speed improvements, especially to Kronecker-factorized multi-output models.
Performance improvements
- Major speed improvements for Kronecker product multitask models (1355, 1430, 1440, 1469, 1477)
- Unwhitened VI speed improvements (1487)
- SGPR speed improvements (1493)
- Large scale exact GP speed improvements (1495)
- Random Fourier feature speed improvements (1446, 1493)
New Features
- Dirichlet Classification likelihood (1484) - based on Milios et al. (NeurIPS 2018)
- MultivariateNormal objects have a `base_sample_shape` attribute for low-rank/degenerate distributions (1502)
New documentation
- Tutorial for designing your own kernels (1421)
Debugging utilities
- Better naming conventions for AdditiveKernel and ProductKernel (1488)
- `gpytorch.settings.verbose_linalg` context manager for seeing what linalg routines are run (1489)
- Unit test improvements (1430, 1437)
Bug Fixes
- `inverse_transform` is applied to the initial values of constraints (1482)
- `psd_safe_cholesky` obeys cholesky_jitter settings (1476)
- fix scaling issue with priors on variational models (1485)
Breaking changes
- `MultitaskGaussianLikelihoodKronecker` (deprecated) is fully incorporated in `MultitaskGaussianLikelihood` (1471)