Changelog for 0.9.5 release #2143
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@esantorella has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
saitcakmak
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Dec 8, 2023
| #### New features | ||
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| Hypervolume Knowledge Gradient (HVKG): | ||
| * Add `qHypervolumeKnowledgeGradient`, which seeks to maximize the difference in hypervolume of the hypervolume-maximizing set of a fixed size after conditioning the unknown observation(s) that would be received if X were evaluated (#1950). |
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I find this sentence a bit hard to follow but looks like this is also what is in the docstring. Probably ok to leave as is.
typo fix from review Co-authored-by: Sait Cakmak <[email protected]>
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@esantorella has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
Balandat
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Dec 8, 2023
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| * Add `qHypervolumeKnowledgeGradient`, which seeks to maximize the difference in hypervolume of the hypervolume-maximizing set of a fixed size after conditioning the unknown observation(s) that would be received if X were evaluated (#1950). | ||
| * Add initializer for one-shot HVKG (#1982). | ||
| * Add tutorial on decoupled Multi-Objective Bayesian Optimization (MOBO) with HVKG (#2094). | ||
| * Illustrate how to use Multi-Fidelity HVKG (MV-HVKG) (#2101). |
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It feels like rather than having a line item for each PR, can we logically group the PRs together?
Suggested change
| * Add `qHypervolumeKnowledgeGradient`, which seeks to maximize the difference in hypervolume of the hypervolume-maximizing set of a fixed size after conditioning the unknown observation(s) that would be received if X were evaluated (#1950). | |
| * Add initializer for one-shot HVKG (#1982). | |
| * Add tutorial on decoupled Multi-Objective Bayesian Optimization (MOBO) with HVKG (#2094). | |
| * Illustrate how to use Multi-Fidelity HVKG (MV-HVKG) (#2101). | |
| * Add `qHypervolumeKnowledgeGradient`, which seeks to maximize the difference in hypervolume of the hypervolume-maximizing set of a fixed size after conditioning the unknown observation(s) that would be received if X were evaluated (#1950, #1982, #2101). | |
| * Add tutorial on decoupled Multi-Objective Bayesian Optimization (MOBO) with HVKG (#2094). |
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@esantorella merged this pull request in ecf9ac1. |
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Motivation
Changelog for 0.9.5 release
Test Plan