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New model analyzes stochastic mirror descent with heavy-tailed noise

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced a continuous-time model for stochastic mirror descent (SMD) that accounts for heavy-tailed noise. This model, termed the Lévy mirror flow (LMF), is designed to analyze SMD's convergence guarantees when subjected to infinite-variance stochastic gradient inputs. The study demonstrates that LMF can still achieve $\epsilon$-optimality within specific time bounds, even with significant jump discontinuities, and provides corresponding discrete-time guarantees for SMD variants. AI

IMPACT Introduces theoretical framework for robust optimization algorithms, potentially improving AI model training stability.

RANK_REASON The cluster contains an academic paper detailing a new theoretical model for optimization algorithms.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New model analyzes stochastic mirror descent with heavy-tailed noise

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The cluster contains an academic paper detailing a new theoretical model for optimization algorithms.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Pierre-Louis Cauvin, Panayotis Mertikopoulos ·

    Bregman meets L\'evy: Stochastic mirror descent with heavy-tailed noise in continuous and discrete time

    arXiv:2606.03769v1 Announce Type: cross Abstract: We study the robustness of stochastic mirror descent (SMD) under heavy-tailed noise, focusing on whether the method retains its convergence guarantees when run with infinite-variance stochastic gradient input. To address this ques…

  2. arXiv cs.LG TIER_1 English(EN) · Panayotis Mertikopoulos ·

    Bregman meets Lévy: Stochastic mirror descent with heavy-tailed noise in continuous and discrete time

    We study the robustness of stochastic mirror descent (SMD) under heavy-tailed noise, focusing on whether the method retains its convergence guarantees when run with infinite-variance stochastic gradient input. To address this question in a principled manner, we begin by introduci…

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