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Found 8 papers, 3 papers with code
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
High-probability analysis of stochastic first-order optimization methods under mild assumptions on the noise has been gaining a lot of attention in recent years.
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance
During recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimization methods has been growing.
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise
In this work, we prove the first high-probability complexity results with logarithmic dependence on the confidence level for stochastic methods for solving monotone and structured non-monotone VIPs with non-sub-Gaussian (heavy-tailed) noise and unbounded domains.
Distributed Methods with Absolute Compression and Error Compensation
Communication compression is a powerful approach to alleviating this issue, and, in particular, methods with biased compression and error compensation are extremely popular due to their practical efficiency.
Near-Optimal High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise
In our paper, we resolve this issue and derive the first high-probability convergence results with logarithmic dependence on the confidence level for non-smooth convex stochastic optimization problems with non-sub-Gaussian (heavy-tailed) noise.
Gradient Clipping Helps in Non-Smooth Stochastic Optimization with Heavy-Tailed Noise
In our paper, we resolve this issue and derive the first high-probability convergence results with logarithmical dependence on the confidence level for non-smooth convex stochastic optimization problems with non-sub-Gaussian (heavy-tailed) noise.
Recent Theoretical Advances in Non-Convex Optimization
For this setting, we first present known results for the convergence rates of deterministic first-order methods, which are then followed by a general theoretical analysis of optimal stochastic and randomized gradient schemes, and an overview of the stochastic first-order methods.
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping
In this paper, we propose a new accelerated stochastic first-order method called clipped-SSTM for smooth convex stochastic optimization with heavy-tailed distributed noise in stochastic gradients and derive the first high-probability complexity bounds for this method closing the gap in the theory of stochastic optimization with heavy-tailed noise.
