"Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited" - Lu Yu, Avetik Karagulyan and Arnak Dalalyan.

International Conference on Learning Representations, 2024

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"Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization" - Hanmin Li, Avetik Karagulyan and Peter Richtárik.

International Conference on Learning Representations, 2024

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"Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition" - Lukang Sun, Avetik Karagulyan and Peter Richtárik.

International Conference on Artificial Intelligence and Statistics. PMLR, 2023.

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"Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets" - Avetik Karagulyan and Arnak S. Dalalyan.

Advances in Neural Information Processing Systems (NeurIPS 2020)

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"Bounding the error of discretized Langevin algorithms for non-strongly log-concave targets" - Arnak Dalalyan, Lionel Riou-Durand and Avetik Karagulyan.

Journal of Machine Learning Research 23(235):1–38, 2022

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"Non-Asymptotic Guarantees for Sampling by Stochastic Gradient Descent." - Avetik Karagulyan.

Journal of Contemporary Mathematical Analysis (Armenian Academy of Sciences) 54.2 (2019): 71-78.

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"User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient" - Arnak S. Dalalyan and Avetik Karagulyan.

Stochastic Processes and their Applications 129.12 (2019): 5278-5311.

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"Existence of positive solutions for an approximation of stationary mean-field games" - Nojood Almayouf, Elena Bachini, Andreia Chapouto, Rita Ferreira, Diogo Gomes, Daniela Jordão, David Evangelista, Avetik Karagulyan, Juan Monasterio, Levon Nurbekyan, Giorgia Pagliar, Marco Piccirilli, Sagar Pratapsi, Mariana Prazeres, João Reis, André Rodrigues, Orlando Romero, Maria Sargsyan, Tommaso Seneci, Chuliang Song, Kengo Terai, Ryota Tomisaki, Hector Velasco-Perez, Vardan Voskanyan, Xianjin Yang.

Involve, a Journal of Mathematics 10.3 (2016): 473-493.

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"Sampling with the Langevin Monte-Carlo" - Avetik Karagulyan. Supervised by Arnak Dalalyan

Thesis was defended at Institut Polytéchnique de Paris in June 2021.

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"SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning" - Avetik Karagulyan, Egor Shulgin, Abdurakhmon Sadiev and Peter Richtárik.

arXiv preprint arXiv:2405.20127, 2024

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"MARINA Meets Matrix Stepsizes: Variance Reduced Distributed Non-Convex Optimization" - Hanmin Li, Avetik Karagulyan and Peter Richtárik.

arXiv preprint arXiv:2310.04614, 2023

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"ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression" - Avetik Karagulyan and Peter Richtárik.

arXiv preprint arXiv:2303.04622, 2023

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