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Stability Based Generalization Bounds for Exponential Family Langevin Dynamics

ICML 2022 · Optimization › Implicit bias and generalization · approach: SGD noise and flat minima · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use gradient discrepancy instead of gradient norms in the stability boundEFLD stability-based generalization bound · Generalization-bound comparisongeneralization bound tightnessHelped
Use O(1/n) rather than O(1/√n) sample dependenceEFLD stability-based generalization bound · Generalization-bound comparisonsample-size dependence: O(1/n) instead of O(1/\sqrt{n})Helped
Use a data-dependent gradient-discrepancy step-size constraint instead of a global-Lipschitz constraintEFLD stability analysis · Step-size boundmaximum allowable step size: \frac{\sigma_t}{L} -> \frac{\sigma_{t}}{\Delta_{t}(\bar{S})}Helped
Compare the proposed bound with Rodríguez-Gálvez et al.'s boundEFLD stability-based generalization bound · Bound comparison in Figure 1generalization bound tightnessHelped
Use expected stability instead of uniform stabilityStability-based generalization analysis · Generalization-bound analysisbound tightnessHelped

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