User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient. - Arnak S. Dalalyan; Avetik G. Karagulyan (CoRR, 2017)

Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets. - Avetik G. Karagulyan; Arnak S. Dalalyan (NeurIPS, 2020)

Bounding the Error of Discretized Langevin Algorithms for Non-Strongly Log-Concave Targets. - Arnak S. Dalalyan; Avetik G. Karagulyan; Lionel Riou-Durand (J. Mach. Learn. Res., 2022)

Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition. - Lukang Sun; Avetik G. Karagulyan; Peter Richtárik (AISTATS, 2023)

Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization. - Hanmin Li; Avetik G. Karagulyan; Peter Richtárik (ICLR, 2024)

Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited. - Lu Yu; Avetik G. Karagulyan; Arnak S. Dalalyan (ICLR, 2024)

SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning. - Avetik G. Karagulyan; Egor Shulgin; Abdurakhmon Sadiev; Peter Richtárik (CoRR, 2024)

ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression. - Avetik G. Karagulyan; Peter Richtárik (UAI, 2025)