Author

Ilgam Latypov

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#machine learning Preprint Oct 2025

Managing Self-Learning Experts under Per-Round Budget Constraints

This paper proposes a novel UCB-type algorithm, M-LCB, designed to manage a pool of self-learning experts in a stochastic environment while accounting for a limited per-round learning budget and derives anytime regret bounds for M-LCB that scale with the individual regrets of the experts.

Ilgam Latypov, A. Suvorikova, Alexey Kroshnin et al. · 1 citation

Preprint Sep 2026

Localize, Restart, Accelerate: Stochastic Optimization under Generalized Smoothness

A two-phase accelerated method that achieves, with high probability, an accelerated optimization contribution and smooth-subclass-optimal statistical dependence on accuracy, up to logarithmic and generalized-smoothness factors.

D. Dvinskikh, A. Gasnikov, A. Lobanov et al. · 2 citations