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Summary: Pull Request resolved: facebook/Ax#747 Pull Request resolved: #990 In NEI and NEHVI, we sample from the posterior of f(X_baseline, X) repeatedly for different X, but the same X_baseline. We can make the sampling more efficient by caching the cholesky decomposition for the posterior covariance matrix of f(X_baseline) and then only compute the bottom q new rows of the cholesky of the posterior covariance matrix f(X_baseline, X). Using the cached cholesky is probably more important for NEHVI than NEI the pruned X_baseline for NEHVI will often be quite a bit larger than the pruned X_baseline for NEI. Reviewed By: Balandat Differential Revision: D25804787 fbshipit-source-id: e16c94fb987dc6b3713bb26b4fe23ec1053ce97c
Summary: Pull Request resolved: #995 see title Reviewed By: Balandat Differential Revision: D32668106 fbshipit-source-id: b0a26781ba184fde5f29ac0a2bd120102b653d1f
Summary: Pull Request resolved: #996 see title Reviewed By: Balandat Differential Revision: D32668194 fbshipit-source-id: c4eba63632c5b4e63fd0ae7db211efb413f49cd1
Summary: There are at least 7 at this year's NeurIPS, not sure I got all of them. Pull Request resolved: #1022 Reviewed By: eytan Differential Revision: D32942709 Pulled By: Balandat fbshipit-source-id: 65634d302d3bb498cc1bd2db02b5fb77203d29aa
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