Papers

Aaron Defazio, Ashok Cutkosky, Harsh Mehta, Konstantin Mishchenko (2023). When, Why and How Much? Adaptive Learning Rate Scheduling by Refinement.

Konstantin Mishchenko, Aaron Defazio (2023). Prodigy: An Expeditiously Adaptive Parameter-Free Learner.

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Konstantin Mishchenko, Rustem Islamov, Eduard Gorbunov, Samuel Horváth (2023). Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity.

Ahmed Khaled, Konstantin Mishchenko, Chi Jin (2023). DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method.

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Blake Woodworth, Konstantin Mishchenko, Francis Bach (2023). Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy.

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Aaron Defazio, Konstantin Mishchenko (2023). Learning-Rate-Free Learning by D-Adaptation.

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Konstantin Mishchenko, Slavomír Hanzely, Peter Richtárik (2023). Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes.

Nikita Doikov, Konstantin Mishchenko, Yurii Nesterov (2022). Super-Universal Regularized Newton Method.

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Samuel Horváth, Konstantin Mishchenko, Peter Richtárik (2022). Adaptive Learning Rates for Faster Stochastic Gradient Methods.

Konstantin Mishchenko, Francis Bach, Mathieu Even, Blake Woodworth (2022). Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays.

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Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich, Peter Richtárik (2022). ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!. ICML.

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Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik (2022). Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization.

Konstantin Mishchenko (2021). Regularized Newton Method with Global $O(1/k^2)$ Convergence.

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Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtárik (2021). IntSGD: Adaptive Floatless Compression of Stochastic Gradients. ICLR.

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Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik (2021). Proximal and Federated Random Reshuffling. ICML.

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Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik (2020). Random Reshuffling: Simple Analysis with Vast Improvements. NeurIPS.

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Adil Salim, Laurent Condat, Konstantin Mishchenko, Peter Richtárik (2020). Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. JOTA.

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Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik (2019). Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates.

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Yura Malitsky, Konstantin Mishchenko (2019). Adaptive Gradient Descent without Descent. ICML.

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Konstantin Mishchenko (2019). Sinkhorn Algorithm as a Special Case of Stochastic Mirror Descent.

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Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik (2019). Tighter Theory for Local SGD on Identical and Heterogeneous Data. AISTATS.

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Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik (2019). First Analysis of Local GD on Heterogeneous Data.

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Konstantin Mishchenko, Mallory Montgomery, Federico Vaggi (2019). A Self-supervised Approach to Hierarchical Forecasting with Applications to Groupwise Synthetic Controls.

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Xun Qian, Alibek Sailanbayev, Konstantin Mishchenko, Peter Richtárik (2019). MISO is Making a Comeback With Better Proofs and Rates.

Saeed Soori, Konstantin Mishchenko, Aryan Mokhtari, Maryam Mehri Dehnavi, Mert Gürbüzbalaban (2019). DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate. AISTATS.

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Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky (2019). Revisiting Stochastic Extragradient. AISTATS.

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Konstantin Mishchenko, Peter Richtárik (2019). A Stochastic Decoupling Method for Minimizing the Sum of Smooth and Non-Smooth Functions.

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Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Sebastian Stich, Peter Richtárik (2019). Stochastic Distributed Learning with Gradient Quantization and Double Variance Reduction. Optimization Methods and Software.

Konstantin Mishchenko, Eduard Gorbunov, Martin Takáč, Peter Richtárik (2019). Distributed Learning with Compressed Gradient Differences.

Konstantin Mishchenko, Filip Hanzely, Peter Richtárik (2019). 99% of Worker-Master Communication in Distributed Optimization Is Not Needed. UAI.

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Konstantin Mishchenko, Peter Richtárik (2018). A Stochastic Penalty Model for Convex and Nonconvex Optimization with Big Constraints.

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Filip Hanzely, Konstantin Mishchenko, Peter Richtárik (2018). SEGA: Variance Reduction via Gradient Sketching. NeurIPS.

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Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick, Massih-Reza Amini (2018). A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning. ICML.

Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick (2018). A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm. SIOPT.

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