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33The release log for BoTorch.
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5+ ## [ 0.7.2] - Sep 27, 2022
6+
7+ #### New Features
8+ * A full refactor of model fitting methods (#1134 ).
9+ * This introduces a new ` fit_gpytorch_mll ` method that multiple-dispatches
10+ on the model type. Users may register custom fitting routines for different
11+ combinations of MLLs, Likelihoods, and Models.
12+ * Unlike previous fitting helpers, ` fit_gpytorch_mll ` does ** not** pass
13+ ` kwargs ` to ` optimizer ` and instead introduces an optional ` optimizer_kwargs `
14+ argument.
15+ * When a model fitting attempt fails, ` botorch.fit ` methods restore modules to their
16+ original states.
17+ * ` fit_gpytorch_mll ` throws a ` ModelFittingError ` when all model fitting attempts fail.
18+ * Upon returning from ` fit_gpytorch_mll ` , ` mll.training ` will be ` True ` if fitting failed
19+ and ` False ` otherwise.
20+ * Allow custom bounds to be passed in to ` SyntheticTestFunction ` (#1415 ).
21+
22+ #### Deprecations
23+ * Deprecate weights argument of risk measures in favor of a ` preprocessing_function ` (#1400 ),
24+ * Deprecate ` fit_gyptorch_model ` ; to be superseded by ` fit_gpytorch_mll ` .
25+
26+ #### Other Changes
27+ * Support risk measures in MOO input constructors (#1401 ).
28+
29+ #### Bug Fixes
30+ * Fix fully Bayesian state dict loading when there are more than 10 models (#1405 ).
31+ * Fix ` batch_shape ` property of ` SaasFullyBayesianSingleTaskGP ` (#1413 ).
32+ * Fix ` model_list_to_batched ` ignoring the ` covar_module ` of the input models (#1419 ).
33+
34+
535## [ 0.7.1] - Sep 13, 2022
636
737#### Compatibility
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