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Overparameterized DMD achieves exponential speedups in convergence for low-rank matrix optimization, even when the target rank is uncertain.
Signal processing offers a surprisingly effective lens for understanding and improving LoRA, the reigning champ of parameter-efficient fine-tuning.
Zeroth-order optimization stability depends on the *entire* Hessian spectrum, not just the largest eigenvalue like first-order methods, offering a new perspective on implicit regularization.
Stop wasting compute: a learned policy can intelligently allocate LLM inference budgets, boosting accuracy by up to 12.8% compared to uniform allocation.
