- Introduced a graph reordering mechanism to improve computational performance on GPUs. - The default starting probability of the marginalized graph kernel is now hyperparameterized and will be optimized by default during training. - Allow users to specify custom starting probability distributions. - Performance improvements due to the in situ computation of starting probabilities instead of loading from memory. - Added `repeat`, `theta_jitter` and `tol` options to the Gaussian process regressor. - Fixed a normalization bug in `GaussianProcessRegressor.fit_loocv`.