26 citations · 91 across the 27 of their papers we have counts for
4 papers · 1 filter
Improving Stochastic Cubic Newton with Momentum
El Mahdi Chayti, Nikita Doikov, Martin Jaggi
We study stochastic second-order methods for solving general non-convex optimization problems. We propose using a special version of momentum to stabilize the stochastic gradient a…
Cubic regularized subspace Newton for non-convex optimization
Jim Zhao, Aurelien Lucchi, Nikita Doikov
This paper addresses the optimization problem of minimizing non-convex continuous functions, which is relevant in the context of high-dimensional machine learning applications char…
Complexity of Minimizing Regularized Convex Quadratic Functions
Daniel Berg Thomsen, Nikita Doikov
In this work, we study the iteration complexity of gradient methods for minimizing convex quadratic functions regularized by powers of Euclidean norms. We show that, due to the uni…
Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions
Nikita Doikov, Sebastian U. Stich, Martin Jaggi
The performance of optimization methods is often tied to the spectrum of the objective Hessian. Yet, conventional assumptions, such as smoothness, do often not enable us to make fi…