Optimizing a regularized failure-probability objective over Gaussian sampler parameters (mean and variance) yields a deterministic Johnson-Lindenstrauss embedding in polynomial time.
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Optimization Can Learn Johnson Lindenstrauss Embeddings
Optimizing a regularized failure-probability objective over Gaussian sampler parameters (mean and variance) yields a deterministic Johnson-Lindenstrauss embedding in polynomial time.