From f998e065a2fef42c59a59c0978b20cd472393e67 Mon Sep 17 00:00:00 2001 From: Sait Cakmak Date: Fri, 15 Jul 2022 09:49:48 -0700 Subject: [PATCH] Update 0.6.5 ChangeLog (#1308) Summary: Pull Request resolved: https://github.com/pytorch/botorch/pull/1308 Adds a few missing entries to 0.6.5 changelog. Reviewed By: esantorella Differential Revision: D37886737 fbshipit-source-id: 261f19d7ecc426049c47236ae4dece5be6dc0f5c --- CHANGELOG.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 07b337eecd..a0ff456f6f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,6 +16,7 @@ The release log for BoTorch. * Add outcome_transform to `FixedNoiseMultiTaskGP` (#1255). * Support Scalable Constrained Bayesian Optimization (#1257). * Support `SaasFullyBayesianSingleTaskGP` in `prune_inferior_points` (#1260). +* Implement MARS as a risk measure (#1303). * Add MARS tutorial (#1305). #### Other Changes @@ -29,15 +30,19 @@ The release log for BoTorch. * Handle `X_pending` properly in `FixedFeatureAcquisition` (#1233, #1234). * PE and PLBO support in Ax (#1240, #1241). * Remove `model.train` call from `get_X_baseline` for better caching (#1289). +* Support `inf` values in `bounds` argument of `optimize_acqf` (#1302). #### Bug Fixes * Update `get_gp_samples` to support input / outcome transforms (#1201). +* Fix cached Cholesky sampling in `qNEHVI` when using `Standardize` outcome transform (#1215). * Make `task_feature` as required input in `MultiTaskGP.construct_inputs` (#1246). * Fix CUDA tests (#1253). * Fix `FixedSingleSampleModel` dtype/device conversion (#1254). * Prevent inappropriate transforms by putting input transforms into train mode before converting models (#1283). * Fix `sample_points_around_best` when using 20 dimensional inputs or `prob_perturb` (#1290). * Skip bound validation in `optimize_acqf` if inequality constraints are specified (#1297). +* Properly handle RFFs when used with a `ModelList` with individual transforms (#1299). +* Update `PosteriorList` to support deterministic-only models and fix `event_shape` (#1300). #### Documentation * Add a note about observation noise in the posterior in `fit_model_with_torch_optimizer` notebook (#1196).