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20242026
most citedHow to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy

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math.OC2026

Near-Optimal Acceleration for Smooth / Nondual Convex First-Order Oracle Optimization

David Martínez-Rubio, Brian Bullins, Cristóbal Guzmán +1

We study the optimization of convex objectives with -Hölder-continuous gradients in over , . (MG26) provides selectors with a movement bound fo…

math.OC2026

Stable Movement for Nondual Lipschitz Convex Optimization: Efficiency and Nearly Optimal Oracle Rates

David Martínez-Rubio, Cristóbal Guzmán

We study efficient algorithms for realizing the first-order oracle complexity of optimization of -Lipschitz convex functions with respect to the -norm over an $\ell_{p…

math.OC2026

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

David Martínez-Rubio, Brian Bullins, Cristóbal Guzmán +1

We study first-order black-box convex optimization over an -ball for objectives Lipschitz in the -norm, solving in the affirmative the nonsmooth version of the COLT…

math.OC2026

Oracle Complexity of Stochastic Fixed-Point Equations with Nonexpansive Maps

Jelena Diakonikolas, Cristóbal Guzmán, David Martínez-Rubio

We study the oracle complexity of computing a point with small fixed-point residual , for a general norm and a self-map of a compact convex set.…

cs.DS2026

Fixed-Parameter Tractability of Private Synthetic Data Generation

Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +3

We study the problem of generating synthetic data under differential privacy. We establish fixed-parameter tractability (FPT) for this problem where the parameter is the treewidth…

cs.CG2026

Computational Hardness of Private Coreset

Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +3

We study the problem of differentially private (DP) computation of coreset for the -means objective. For a given input set of points, a coreset is another set of points such tha…