Savelii Chezhegov

Convergence of Clipped-SGD for Convex $(L_0,L_1)$-Smooth Optimization with Heavy-Tailed Noise

Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation

Complexity-Separated Schemes for Addressing Structured Heterogeneity in Federated Learning

Aligning Distributionally Robust Optimization with Practical Deep Learning Needs

High-Probability Bounds for the Last Iterate of Clipped SGD

Differentially Private Clipped-SGD: High-Probability Convergence with Arbitrary Clipping Level

Clipping Improves Adam and AdaGrad when the Noise Is Heavy-Tailed

Decentralized Finite-Sum Optimization over Time-Varying Networks

Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed