Putting MRFs on a Tensor Train. - Alexander Novikov 001; Anton Rodomanov; Anton Osokin; Dmitry P. Vetrov (ICML, 2014)

Primal-Dual Method for Searching Equilibrium in Hierarchical Congestion Population Games. - Pavel E. Dvurechensky; Alexander V. Gasnikov; Evgenia Gasnikova; Sergey Matsievsky; Anton Rodomanov; Inna Usik (DOOR (Supplement), 2016)

A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums. - Anton Rodomanov; Dmitry Kropotov (ICML, 2016)

Smoothness Parameter of Power of Euclidean Norm. - Anton Rodomanov; Yurii E. Nesterov (J. Optim. Theory Appl., 2020)

A Randomized Coordinate Descent Method with Volume Sampling. - Anton Rodomanov; Dmitry Kropotov (SIAM J. Optim., 2020)

New Results on Superlinear Convergence of Classical Quasi-Newton Methods. - Anton Rodomanov; Yurii E. Nesterov (J. Optim. Theory Appl., 2021)

Greedy Quasi-Newton Methods with Explicit Superlinear Convergence. - Anton Rodomanov; Yurii E. Nesterov (SIAM J. Optim., 2021)

Rates of superlinear convergence for classical quasi-Newton methods. - Anton Rodomanov; Yurii E. Nesterov (Math. Program., 2022)

Quasi-Newton methods with provable efficiency guarantees. - Anton Rodomanov (2022)

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

Subgradient ellipsoid method for nonsmooth convex problems. - Anton Rodomanov; Yurii E. Nesterov (Math. Program., 2023)

Non-convex Stochastic Composite Optimization with Polyak Momentum. - Yuan Gao; Anton Rodomanov; Sebastian U. Stich (ICML, 2024)

Federated Optimization with Doubly Regularized Drift Correction. - Xiaowen Jiang; Anton Rodomanov; Sebastian U. Stich (ICML, 2024)

Universal Gradient Methods for Stochastic Convex Optimization. - Anton Rodomanov; Ali Kavis; Yongtao Wu; Kimon Antonakopoulos; Volkan Cevher (ICML, 2024)

Stabilized Proximal-Point Methods for Federated Optimization. - Xiaowen Jiang; Anton Rodomanov; Sebastian U. Stich (NeurIPS, 2024)

Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction. - Anton Rodomanov; Xiaowen Jiang; Sebastian U. Stich (NeurIPS, 2024)

Optimizing (L0, L1)-Smooth Functions by Gradient Methods. - Daniil Vankov; Anton Rodomanov; Angelia Nedich; Lalitha Sankar; Sebastian U. Stich (ICLR, 2025)

DADA: Dual Averaging with Distance Adaptation. - Mohammad Moshtaghifar; Anton Rodomanov; Daniil Vankov; Sebastian U. Stich (CoRR, 2025)

Decoupled SGDA for Games with Intermittent Strategy Communication. - Ali Zindari; Parham Yazdkhasti; Anton Rodomanov; Tatjana Chavdarova; Sebastian U. Stich (CoRR, 2025)

Accelerated Distributed Optimization with Compression and Error Feedback. - Yuan Gao; Anton Rodomanov; Jeremy Rack; Sebastian U. Stich (CoRR, 2025)

Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization. - Yuki Takezawa; Xiaowen Jiang; Anton Rodomanov; Sebastian U. Stich (CoRR, 2025)