Randomized Block Cubic Newton Method. - Nikita Doikov; Peter Richtárik (ICML, 2018)

Inexact Tensor Methods with Dynamic Accuracies. - Nikita Doikov; Yurii E. Nesterov (ICML, 2020)

Stochastic Subspace Cubic Newton Method. - Filip Hanzely; Nikita Doikov; Yurii E. Nesterov; Peter Richtárik (ICML, 2020)

Convex optimization based on global lower second-order models. - Nikita Doikov; Yurii E. Nesterov (NeurIPS, 2020)

Contracting Proximal Methods for Smooth Convex Optimization. - Nikita Doikov; Yurii E. Nesterov (SIAM J. Optim., 2020)

Minimizing Uniformly Convex Functions by Cubic Regularization of Newton Method. - Nikita Doikov; Yurii E. Nesterov (J. Optim. Theory Appl., 2021)

New second-order and tensor methods in convex optimization. - Nikita Doikov (2021)

Local convergence of tensor methods. - Nikita Doikov; Yurii E. Nesterov (Math. Program., 2022)

High-Order Optimization Methods for Fully Composite Problems. - Nikita Doikov; Yurii E. Nesterov (SIAM J. Optim., 2022)

Linearization Algorithms for Fully Composite Optimization. - Maria-Luiza Vladarean; Nikita Doikov; Martin Jaggi; Nicolas Flammarion (COLT, 2023)

Second-Order Optimization with Lazy Hessians. - Nikita Doikov; El Mahdi Chayti; Martin Jaggi (ICML, 2023)

Polynomial Preconditioning for Gradient Methods. - Nikita Doikov; Anton Rodomanov (ICML, 2023)

Shuffle SGD is Always Better than SGD: Improved Analysis of SGD with Arbitrary Data Orders. - Anastasia Koloskova; Nikita Doikov; Sebastian U. Stich; Martin Jaggi (CoRR, 2023)

Minimizing Quasi-Self-Concordant Functions by Gradient Regularization of Newton Method. - Nikita Doikov (CoRR, 2023)

Affine-invariant contracting-point methods for Convex Optimization. - Nikita Doikov; Yurii E. Nesterov (Math. Program., 2023)

Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions. - Nikita Doikov; Sebastian U. Stich; Martin Jaggi (ICML, 2024)

On Convergence of Incremental Gradient for Non-convex Smooth Functions. - Anastasia Koloskova; Nikita Doikov; Sebastian U. Stich; Martin Jaggi (ICML, 2024)

Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods. - El Mahdi Chayti; Martin Jaggi; Nikita Doikov (Trans. Mach. Learn. Res., 2024)

Gradient regularization of Newton method with Bregman distances. - Nikita Doikov; Yurii E. Nesterov (Math. Program., 2024)

Super-Universal Regularized Newton Method. - Nikita Doikov; Konstantin Mishchenko; Yurii E. Nesterov (SIAM J. Optim., 2024)

Improving Stochastic Cubic Newton with Momentum. - El Mahdi Chayti; Nikita Doikov; Martin Jaggi (AISTATS, 2025)

Cubic regularized subspace Newton for non-convex optimization. - Jim Zhao; Nikita Doikov; Aurélien Lucchi (AISTATS, 2025)

First and Zeroth-Order Implementations of the Regularized Newton Method with Lazy Approximated Hessians. - Nikita Doikov; Geovani Nunes Grapiglia (J. Sci. Comput., 2025)

Gradient-Normalized Smoothness for Optimization with Approximate Hessians. - Andrei Semenov; Martin Jaggi; Nikita Doikov (CoRR, 2025)