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New theory bounds transient amplification in coupled gradient descent

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new pseudospectral theory to analyze transient amplification in coupled gradient descent, a method used in bilevel optimization and adversarial training. The theory provides sharp bounds for block-triangular Jacobians, revealing a non-asymptotic learning dynamics regime previously hidden by spectral-radius analysis. Experiments on various problems, including neural network training, confirm the theory's validity. AI

IMPACT Provides a new analytical tool for understanding complex optimization dynamics in high-dimensional learning.

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework and experimental validation.

Read on arXiv stat.ML →

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New theory bounds transient amplification in coupled gradient descent

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Ahanaf Hasan Ariq ·

    Pseudospectral Bounds for Transient Amplification in Coupled Gradient Descent

    arXiv:2606.04031v1 Announce Type: cross Abstract: Coupled gradient descent--where the update of one parameter block depends on another--underlies bilevel optimization, two-time-scale stochastic approximation, and adversarial training. When the coupled Jacobian is block-triangular…

  2. arXiv stat.ML TIER_1 English(EN) · Ahanaf Hasan Ariq ·

    Pseudospectral Bounds for Transient Amplification in Coupled Gradient Descent

    Coupled gradient descent--where the update of one parameter block depends on another--underlies bilevel optimization, two-time-scale stochastic approximation, and adversarial training. When the coupled Jacobian is block-triangular, asymptotic stability is governed by the spectral…

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