arXiv Statistics ML
Tight Transition Time Bounds for Separable Logistic Regression at the Edge of Stability
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Tight Bounds for Logistic Regression with Large Stepsize Gradient Descent in Low Dimension
arXiv:2602. 12471v2 Announce Type: replace Abstract: We consider the optimization problem of minimizing the logistic loss with gradient descent to train a linear model for binary classification with separable data.
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Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes
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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization
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Nonlinear Two-Time-Scale Stochastic Approximation: A Sharp Phase Transition and How to Beat It
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Hugging Face Trending Papers
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The Sharp Tail of Uniform Stability
Uniform stability controls how much one training example can change the loss at any test point. A new logarithmic-free upper bound shows that a $γ$-uniformly stable algorithm with loss in $[0,L]$ has...
arXiv Machine Learning
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Accelerating Min-Max Optimization via Power-Law Stepsizes
arXiv:2606. 01764v1 Announce Type: cross Abstract: We revisit the convergence guarantees of the Extragradient (EG) method for unconstrained biaffine min-max optimization.