91 citations · 113 across the 41 of their papers we have counts for
6 papers · 1 filter
Advancing the lower bounds: An accelerated, stochastic, second-order method with optimal adaptation to inexactness
Artem Agafonov, Dmitry Kamzolov, Alexander Gasnikov +4
We present a new accelerated stochastic second-order method that is robust to both gradient and Hessian inexactness, which occurs typically in machine learning. We establish theore…
Convergence analysis of stochastic gradient descent with adaptive preconditioning for non-convex and convex functions
Dmitrii A. Pasechnyuk, Alexander Gasnikov, Martin Takáč
Preconditioning is a crucial operation in gradient-based numerical optimisation. It helps decrease the local condition number of a function by appropriately transforming its gradie…
Information-Theoretic Upper Bounds for Deterministic Noise in Zeroth-Order Convex Optimization
Dmitry Pasechnyuk-Vilensky, Igor Pavlov, Martin Takáč +1
We study zeroth-order convex optimization on Euclidean balls with function values corrupted by a fixed, uniformly bounded deterministic perturbation. For a query budget , the ma…
Hybrid Methods in Polynomial Optimisation
Johannes Aspman, Gilles Bareilles, Vyacheslav Kungurtsev +2
The Moment/Sum-of-squares hierarchy provides a way to compute the global minimizers of polynomial optimization problems (POP), at the cost of solving a sequence of increasingly lar…
Cubic Regularization is the Key! The First Accelerated Quasi-Newton Method with a Global Convergence Rate of for Convex Functions
Dmitry Kamzolov, Klea Ziu, Artem Agafonov +1
In this paper, we propose the first Quasi-Newton method with a global convergence rate of for general convex functions. Quasi-Newton methods, such as BFGS, SR-1, are we…
Learning Confident Classifiers in the Presence of Label Noise
Asma Ahmed Hashmi, Aigerim Zhumabayeva, Nikita Kotelevskii +4
The success of Deep Neural Network (DNN) models significantly depends on the quality of provided annotations. In medical image segmentation, for example, having multiple expert ann…