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High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds

NeurIPS 2020 · Probabilistic and scientific modeling › Bayesian optimization · approach: Local and high-dimensional Bayesian optimization · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use HD-GaBO instead of competing BO approachesSphere-manifold optimization · Product-of-sines on S^5 embedded in S^50log simple regretHelped
Use HD-GaBO instead of competing BO approachesSPD-manifold optimization · Styblinski-Tang on SPD S++^3 embedded in S++^10log simple regretHelped
Use HD-GaBO instead of competing BO approachesSphere-manifold optimization · Rosenbrock on S^5 embedded in S^50log simple regretMixed
Use HD-GaBO instead of competing BO approachesSphere-manifold optimization · Ackley on S^5 embedded in S^50log simple regretNo clear effect
Use geometry-aware optimization rather than geometry-unaware BOSphere-manifold optimization · Product-of-sines on S^5 embedded in S^50log simple regretHelped
Use Cholesky-based parameterization instead of direct Euclidean parameterizationEuclidean BO methods on SPD matrices · Rosenbrock, Styblinski-Tang, and product-of-sines on SPD S++^3 embedded in S++^10log simple regretHelped
Use HD-GaBO instead of competing BO approachesSPD-manifold optimization · Rosenbrock on SPD S++^3 embedded in S++^10log simple regretMixed
Use HD-GaBO instead of competing BO approachesSphere-manifold optimization · Rosenbrock, Ackley, and product-of-sines on S^5 embedded in S^70log simple regretMixed
Use HD-GaBO instead of competing BO approachesSPD-manifold optimization · Rosenbrock, Styblinski-Tang, and product-of-sines on SPD S++^3 embedded in S++^12log simple regretMixed

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