Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping. - Eduard Gorbunov; Marina Danilova; Alexander V. Gasnikov (NeurIPS, 2020)

Recent Theoretical Advances in Non-Convex Optimization. - Marina Danilova; Pavel E. Dvurechensky; Alexander V. Gasnikov; Eduard Gorbunov; Sergey Guminov; Dmitry Kamzolov; Innokentiy Shibaev (CoRR, 2020)

Near-Optimal High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise. - Eduard Gorbunov; Marina Danilova; Innokentiy Shibaev; Pavel E. Dvurechensky; Alexander V. Gasnikov (CoRR, 2021)

Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise. - Eduard Gorbunov; Marina Danilova; David Dobre; Pavel E. Dvurechenskii; Alexander V. Gasnikov; Gauthier Gidel (NeurIPS, 2022)

Distributed Methods with Absolute Compression and Error Compensation. - Marina Danilova; Eduard Gorbunov (CoRR, 2022)

High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance. - Abdurakhmon Sadiev; Marina Danilova; Eduard Gorbunov; Samuel Horváth; Gauthier Gidel; Pavel E. Dvurechensky; Alexander V. Gasnikov; Peter Richtárik (ICML, 2023)

High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise. - Eduard Gorbunov; Abdurakhmon Sadiev; Marina Danilova; Samuel Horváth; Gauthier Gidel; Pavel E. Dvurechensky; Alexander V. Gasnikov; Peter Richtárik (ICML, 2024)

High-Probability Complexity Bounds for Non-smooth Stochastic Convex Optimization with Heavy-Tailed Noise. - Eduard Gorbunov; Marina Danilova; Innokentiy Shibaev; Pavel E. Dvurechensky; Alexander V. Gasnikov (J. Optim. Theory Appl., 2024)