Memoryless stability-annealed smoothed-sign descent with weighted exponential loss converges in normalized iterates to a Burg-type barrier minimizer on a margin slice, with an explicit S_t^{-1/2} envelope.
Proceedings of Thirty Third Conference on Learning Theory , pages =
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2026 2verdicts
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HORST uses non-commutative operator composition and a hyperbolic mirror map to combine stability from adaptive optimizers with L1 sparsity bias, outperforming AdamW across sparsity levels on vision and language tasks.
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Stability Annealing Selects the Implicit Bias of Smoothed Sign Descent: A Rate-Indexed Barrier Path on Separable Data
Memoryless stability-annealed smoothed-sign descent with weighted exponential loss converges in normalized iterates to a Burg-type barrier minimizer on a margin slice, with an explicit S_t^{-1/2} envelope.
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HORST: Composing Optimizer Geometries for Sparse Transformer Training
HORST uses non-commutative operator composition and a hyperbolic mirror map to combine stability from adaptive optimizers with L1 sparsity bias, outperforming AdamW across sparsity levels on vision and language tasks.