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New research explores quantum and Frank-Wolfe methods for heavy-tailed noise optimization

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore optimization techniques under heavy-tailed noise conditions. The first paper, "Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise," proposes novel quantum mean estimators and a quantum normalized stochastic gradient descent method (QNSGD) that offer improved query complexity for certain optimization problems. The second paper, "Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise," reinterprets recent optimizers like Lion and Muon as instances of Stochastic Frank-Wolfe and develops robust variants to better handle heavy-tailed gradient noise, providing new theoretical guarantees. AI

IMPACT These papers advance theoretical understanding of optimization methods, potentially leading to more efficient and robust AI model training in scenarios with noisy gradients.

RANK_REASON Two academic papers published on arXiv detailing novel optimization techniques for machine learning.

Read on arXiv cs.LG →

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

New research explores quantum and Frank-Wolfe methods for heavy-tailed noise optimization

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Bin Luo, Chengchang Liu, Jonathan Allcock, Shengyu Zhang, John C. S. Lui ·

    Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise

    arXiv:2607.25492v2 Announce Type: replace Abstract: We study stochastic optimization with heavy-tailed gradient noise. We first propose a novel quantum mean estimator for multivariate heavy-tailed random variables that achieves lower query complexity than optimal classical estima…

  2. arXiv stat.ML TIER_1 English(EN) · Maria-Eleni Sfyraki, Jun-Kun Wang ·

    Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise

    arXiv:2506.04192v3 Announce Type: replace-cross Abstract: Stochastic Frank-Wolfe is a classical optimization method for solving constrained optimization problems. On the other hand, recent optimizers such as Lion and Muon have gained quite significant popularity in deep learning.…

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