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math.OC2025
Loss-Transformation Invariance in the Damped Newton Method
Alexander Shestakov, Sushil Bohara, Samuel Horváth +2
The Newton method is a powerful optimization algorithm, valued for its rapid local convergence and elegant geometric properties. However, its theoretical guarantees are usually lim…
math.OC2025
Convergence of Clipped-SGD for Convex -Smooth Optimization with Heavy-Tailed Noise
Savelii Chezhegov, Aleksandr Beznosikov, Samuel Horváth +1
Gradient clipping is a widely used technique in Machine Learning and Deep Learning (DL), known for its effectiveness in mitigating the impact of heavy-tailed noise, which frequentl…