Clipped AdamW with exponentially weighted accumulation achieves superior global convergence rates for convex stochastic generalized Lipschitz optimization compared to SGD and AdaGrad.
Random scaling and momentum for non-smooth non-convex optimization
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StoSignSGD resolves SignSGD divergence on non-smooth objectives via structural stochasticity, matching optimal convex rates and improving non-convex bounds while delivering 1.44-2.14x speedups in FP8 LLM pretraining.
Proving stability of Leon's preconditioner enables the first tuning-free Nesterov-accelerated projection-free adaptive SGD variant with improved non-smooth non-convex rates.
FOAM adaptively controls damping and update frequency in Shampoo based on staleness-oriented error approximation to cut wall-clock time while preserving convergence.
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Optimal Projection-Free Adaptive SGD for Matrix Optimization
Proving stability of Leon's preconditioner enables the first tuning-free Nesterov-accelerated projection-free adaptive SGD variant with improved non-smooth non-convex rates.