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New PS-Clip-VMC algorithm enhances robustness in stochastic optimization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new variant of the Variational Monte Carlo (VMC) algorithm, named PS-Clip-VMC, designed to improve robustness in stochastic optimization. This new method addresses issues where estimators for local energy and gradients are often heavy-tailed and lack higher moments, which can hinder convergence. PS-Clip-VMC achieves convergence in expectation and with high probability by clipping these random variables. Initial experiments with FermiNet on atomic systems show significant robustness improvements over standard VMC methods. AI

IMPACT Introduces a more robust optimization technique that could improve training stability for complex models in scientific computing.

RANK_REASON Academic paper detailing a new algorithm and its theoretical properties. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PS-Clip-VMC algorithm enhances robustness in stochastic optimization

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Davide Nobile ·

    Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization

    Variational Monte Carlo (VMC) is a central algorithm in electronic structure theory and has gained renewed importance through modern neural-network ansätze such as FermiNet. At its core, VMC seeks ground states by minimizing the Rayleigh quotient by stochastic optimization. In th…

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