Eduard Gorbunov

Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions UAI 2026 Who to Trust? Aggregating Client Predictions in Federated Distillation. UAI 2026 Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed ICML 2025 EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback JMLR 2025 Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity ICLR 2025 Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization ICLR 2025 Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems AISTATS 2024 Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences NeurIPS 2024 Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates AISTATS 2024 Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences NeurIPS 2024 Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations NeurIPS 2024 High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise ICML 2024 Low-Resource Machine Translation through the Lens of Personalized Federated Learning EMNLP 2024 Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad NeurIPS 2024 Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance NeurIPS 2023 Byzantine-Tolerant Methods for Distributed Variational Inequalities NeurIPS 2023 Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity ICML 2023 High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance ICML 2023 Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions NeurIPS 2023 Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods AISTATS 2023 Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top ICLR 2023 3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation ICML 2022 Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise NeurIPS 2022 Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity AISTATS 2022 Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities NeurIPS 2022 Secure Distributed Training at Scale ICML 2022 Stochastic Extragradient: General Analysis and Improved Rates AISTATS 2022 Local SGD: Unified Theory and New Efficient Methods AISTATS 2021 MARINA: Faster Non-Convex Distributed Learning with Compression ICML 2021 Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices NeurIPS 2021 A Stochastic Derivative Free Optimization Method with Momentum ICLR 2020 A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent AISTATS 2020 Linearly Converging Error Compensated SGD NeurIPS 2020 Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping NeurIPS 2020 Near Optimal Methods for Minimizing Convex Functions with Lipschitz p-th Derivatives COLT 2019 Optimal Tensor Methods in Smooth Convex and Uniformly ConvexOptimization COLT 2019 Stochastic Spectral and Conjugate Descent Methods NeurIPS 2018