BOOOM parametrizes Stiefel manifold optimization into Euclidean angle space using global Givens rotations and solves it with recursive modified pattern search for loss-agnostic black-box problems.
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StrEBM applies source-wise Gaussian-process-inspired energies with learnable length-scales to jointly optimize latent trajectories and observation mappings for recovering components from linear and nonlinear mixtures.
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BOOOM: Loss-Function-Agnostic Black-Box Optimization over Orthonormal Manifolds for Machine Learning and Statistical Inference
BOOOM parametrizes Stiefel manifold optimization into Euclidean angle space using global Givens rotations and solves it with recursive modified pattern search for loss-agnostic black-box problems.
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StrEBM: A Structured Latent Energy-Based Model for Blind Source Separation
StrEBM applies source-wise Gaussian-process-inspired energies with learnable length-scales to jointly optimize latent trajectories and observation mappings for recovering components from linear and nonlinear mixtures.