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Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent

ICML 2021 · Optimization › Implicit bias and generalization · approach: SGD noise and flat minima · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the exact discrete-time SGD covariance instead of the continuous-time approximationSGD with white noise · 1D quadratic lossvariance prediction agreementHelped
Use the exact discrete-time SGD covariance instead of the continuous-time approximationSGD with white noise · 2D quadratic lossparameter fluctuation prediction agreementHelped
Use the exact discrete-time SGD covariance instead of the continuous-time approximationSGD with minibatch noise · 1D linear regressionvariance prediction agreementHelped
Use the exact discrete-time SGD covariance instead of the continuous-time approximationSGD with minibatch noise · 2D linear regressionparameter fluctuation prediction agreementHelped
Approximate minibatch noise covariance as proportional to the HessianDiscrete-time SGD theory · Minibatch-noise experimentsvariance prediction agreementHurt
Use the discrete-time momentum theory instead of the continuous-time approximationSGD with momentum and white noise · 1D quadratic loss with λk = 2.75variance prediction agreementHelped
Use the discrete-time momentum theory instead of the continuous-time approximationSGD with momentum and minibatch noise · Linear regression with λk = 2.75variance prediction agreementHelped
Use the discrete-time escaping-efficiency prediction instead of the continuous-time predictionSGD · Sharp-minimum escape experimentescaping efficiency prediction agreementHelped
Use the discrete-time Kramers escape-rate approximation instead of the continuous-time predictionSGD · Escape experiment with rescaled lossescape rate trendHelped
Use a discrete-time SGD theory that permits finite-covariance non-Gaussian noiseSGD · 1D quadratic loss with Student's t and centered χ² noisevariance prediction agreementHelped

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