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Nikita Doikov
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2020 – today
- 2024
- [j7]Nikita Doikov
, Yurii E. Nesterov
Gradient regularization of Newton method with Bregman distances. Math. Program. 204(1): 1-25 (2024) - [j6]Nikita Doikov
Super-Universal Regularized Newton Method. SIAM J. Optim. 34(1): 27-56 (2024) - [c9]Nikita Doikov, Sebastian U. Stich, Martin Jaggi:
Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions. ICML 2024 - [c8]Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi:
On Convergence of Incremental Gradient for Non-convex Smooth Functions. ICML 2024 - [i11]Jim Zhao, Aurélien Lucchi, Nikita Doikov:
Cubic regularized subspace Newton for non-convex optimization. CoRR abs/2406.16666 (2024) - [i10]El Mahdi Chayti, Nikita Doikov, Martin Jaggi:
Improving Stochastic Cubic Newton with Momentum. CoRR abs/2410.19644 (2024) - 2023
- [j5]Nikita Doikov
Affine-invariant contracting-point methods for Convex Optimization. Math. Program. 198(1): 115-137 (2023) - [c7]Maria-Luiza Vladarean, Nikita Doikov, Martin Jaggi, Nicolas Flammarion:
Linearization Algorithms for Fully Composite Optimization. COLT 2023: 3669-3695 - [c6]Nikita Doikov, El Mahdi Chayti, Martin Jaggi:
Second-Order Optimization with Lazy Hessians. ICML 2023: 8138-8161 - [c5]Nikita Doikov, Anton Rodomanov:
Polynomial Preconditioning for Gradient Methods. ICML 2023: 8162-8187 - [i9]Nikita Doikov, Anton Rodomanov:
Polynomial Preconditioning for Gradient Methods. CoRR abs/2301.13194 (2023) - [i8]El Mahdi Chayti, Nikita Doikov, Martin Jaggi:
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods. CoRR abs/2302.11962 (2023) - [i7]Maria-Luiza Vladarean, Nikita Doikov, Martin Jaggi, Nicolas Flammarion:
Linearization Algorithms for Fully Composite Optimization. CoRR abs/2302.12808 (2023) - [i6]Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi:
Shuffle SGD is Always Better than SGD: Improved Analysis of SGD with Arbitrary Data Orders. CoRR abs/2305.19259 (2023) - [i5]Nikita Doikov:
Minimizing Quasi-Self-Concordant Functions by Gradient Regularization of Newton Method. CoRR abs/2308.14742 (2023) - [i4]Nikita Doikov, Geovani Nunes Grapiglia:
First and zeroth-order implementations of the regularized Newton method with lazy approximated Hessians. CoRR abs/2309.02412 (2023) - 2022
- [j4]Nikita Doikov
Local convergence of tensor methods. Math. Program. 193(1): 315-336 (2022) - [j3]Nikita Doikov
High-Order Optimization Methods for Fully Composite Problems. SIAM J. Optim. 32(3): 2402-2427 (2022) - [i3]Nikita Doikov, Konstantin Mishchenko, Yurii E. Nesterov:
Super-Universal Regularized Newton Method. CoRR abs/2208.05888 (2022) - [i2]Nikita Doikov, El Mahdi Chayti, Martin Jaggi:
Second-order optimization with lazy Hessians. CoRR abs/2212.00781 (2022) - 2021
- [j2]Nikita Doikov
Minimizing Uniformly Convex Functions by Cubic Regularization of Newton Method. J. Optim. Theory Appl. 189(1): 317-339 (2021) - 2020
- [j1]Nikita Doikov
Contracting Proximal Methods for Smooth Convex Optimization. SIAM J. Optim. 30(4): 3146-3169 (2020) - [c4]Nikita Doikov, Yurii E. Nesterov:
Inexact Tensor Methods with Dynamic Accuracies. ICML 2020: 2577-2586 - [c3]Filip Hanzely, Nikita Doikov, Yurii E. Nesterov, Peter Richtárik:
Stochastic Subspace Cubic Newton Method. ICML 2020: 4027-4038 - [c2]Nikita Doikov, Yurii E. Nesterov:
Convex optimization based on global lower second-order models. NeurIPS 2020 - [i1]Filip Hanzely
Stochastic Subspace Cubic Newton Method. CoRR abs/2002.09526 (2020)
2010 – 2019
- 2018
- [c1]Nikita Doikov, Peter Richtárik:
Randomized Block Cubic Newton Method. ICML 2018: 1289-1297
Coauthor Index
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