Author

Martin Takác

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Preprint Oct 2026

Matching the Lower Bounds: Stochastic Contracting Cubic Newton and Its Optimal Acceleration

We study second-order methods for convex stochastic optimization, where gradients and Hessians are available only through stochastic estimates with variances $\sigma_1^2$ and $\sigma_2^2$, respectively. First, we propose the Stochastic Contracting Cubic Newton method. At each iteration, it minimizes a cubic model with...

A. Agafonov, Аслан Нажмудинович Ашабоков, D. Kamzolov et al. · 0 citations

Preprint Sep 2026

Application of Optimal Inexact Second-Order Acceleration to Distributed Stochastic Optimization under Statistical Similarity

We consider distributed stochastic convex optimization with a fixed budget of $N$ independent samples split among $m$ workers. Sample average approximation reduces the problem to a regularized finite-sum problem whose local Hessians are statistically similar. This allows the Hessian of the local objective at the server...

Yury A. Sokolov, Maxim Mashtaler, A. Gasnikov et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Neither Adversarial Training Nor Purification: Emergent Adversarial Robustness from Oscillatory Predictive Learning

This work introduces Oscillatory Predictive Learning (OPL), a two-stage framework that combines Artificial Kuramoto Oscillatory Neurons (AKOrN) with predictive self-supervised pretraining using X-PhiNet and compares it with other randomized adversarial defense methods that provide precise, reproducible, and strong atta...

M. Habibi, Klea Ziu, Martin Takác et al. · 0 citations