Unifies ES, CBO, and OVI black-box optimizers via two design axes and proposes hybrid methods that outperform base algorithms on benchmarks.
arXiv preprint arXiv:2212.04180 , year=
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Derives optimality constraints for nonnegative joint dictionary learning that explain observed SAE behaviors such as feature splitting, absorption, and dense antipodal features.
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Bridging Spherical Black-Box Optimizers
Unifies ES, CBO, and OVI black-box optimizers via two design axes and proposes hybrid methods that outperform base algorithms on benchmarks.
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How Optimality Structures Sparse Dictionaries: A Theory for Understanding SAE Representations
Derives optimality constraints for nonnegative joint dictionary learning that explain observed SAE behaviors such as feature splitting, absorption, and dense antipodal features.