Author

Brian Bullins

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#machine learning Preprint Sep 2026

Near-Optimal Acceleration for Smooth $\ell_p$ / $\ell_q$ Nondual Convex First-Order Oracle Optimization

We study the optimization of convex objectives with $(L,\kappa-1)$-H\"older-continuous gradients in $\ell_q$ over $R B_p^d$, $1<\kappa\le 2$. (MG26) provides selectors with a movement bound for the problem of chasing high-dimensional convex nested sets for every $p<q$ and generally reduces Lipschitz convex optimization...

David Martínez-Rubio, Brian Bullins, Cristóbal Guzmán et al. · 0 citations

#machine learning Preprint Sep 2026

The First-Order Oracle Complexity of Lipschitz Convex Optimization in Nondual Settings

We study first-order black-box convex optimization over an $\ell_p$-ball for objectives Lipschitz in the $\ell_q$-norm, solving in the affirmative the nonsmooth version of the COLT open question (Guz15b) on whether the geometry of a smaller feasible set ($p<q$) can improve convergence rates in convex optimization, and...

David Martínez-Rubio, Brian Bullins, Cristóbal Guzmán et al. · 1 citation

#machine learning Preprint Aug 2026

Spectral Saliency for Machine Unlearning

Inspired by Muon, this work adopts the spectral view for unlearning and proposes Spectral Saliency Unlearning (SSU), a thresholding approach that thresholds weak singular components and updates only those directions supported by a confident unlearning signal.

Cedar Site Bai, Amber Yijia Zheng, Raymond A. Yeh et al. · 0 citations