Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Changelog for version 0.4.0 #715

Closed
wants to merge 3 commits into from
Closed
Changes from 2 commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
42 changes: 42 additions & 0 deletions CHANGELOG.md
Expand Up @@ -3,6 +3,48 @@
The release log for BoTorch.


## [0.4.0] - Feb 23, 2021

#### Compatibility
* Require PyTorch >=1.7.1 (#714).
* Require GPyTorch >=1.4 (#714).

#### New Features
* `HigherOrderGP` - High-Order Gaussian Process (HOGP) model for
high-dimensional output regression (#631, #646, #648, #680).
* `qMultiStepLookahead` acquisition function for general look-ahead
optimization approaches (#611, #659).
* `ScalarizedPosteriorMean` and `project_to_sample_points` for more
advanced MFKG functionality (#645).
* Large-scale Thompson sampling tutorial (#654, #713).
* Tutorial for optimizing mixed continuous/discrete domains (application
to multi-fidelity KG with discrete fidelities) (#716).
* `GPDraw` utility for sampling from (exact) GP priors (#655).
* Add `X` as optional arg to call signature of `MCAcqusitionObjective` (#487).
* Add Add `OSY` synthetic test problem (#679).

#### Bug Fixes
* Fix matrix multiplication in `scalarize_posterior` (#638).
* Set `X_pending` in `get_acquisition_function` in `qEHVI` (#662).
* Make contextual kernel device-aware (#666).
* Do not use an `MCSampler` in `MaxPosteriorSampling` (#701).
* Add ability to subset outcome transforms (#711).

#### Performance Improvements
* Batchify box decomposition for 2d case (#642).

#### Other Changes
* Use scipy distribution in MES quantile bisect (#633).
* Use new closure definition for GPyTorch priors (#634).
* Allow enabling of approximate root decomposition in `posterior` calls (#652).
* Support for upcoming 21201-dimensional PyTorch `SobolEngine` (#672, #674).
* Refactored various MOO utilities to allow future additions (#656, #657, #658, #661).
* Support input_transform in PairwiseGP (#632).
* Output shape checks for t_batch_mode_transform (#577).
* Check for NaN in `gen_candidates_scipy` (#688).
* Introduce `base_sample_shape` property to `Posterior` objects (#718).


## [0.3.3] - Dec 8, 2020

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