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

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

Distributed Learning with Compressed Gradient Differences. - Konstantin Mishchenko; Eduard Gorbunov; Martin Takác 001; Peter Richtárik (CoRR, 2019)

99% of Parallel Optimization is Inevitably a Waste of Time. - Konstantin Mishchenko; Filip Hanzely; Peter Richtárik (CoRR, 2019)

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

First Analysis of Local GD on Heterogeneous Data. - Ahmed Khaled 001; Konstantin Mishchenko; Peter Richtárik (CoRR, 2019)

Better Communication Complexity for Local SGD. - Ahmed Khaled 001; Konstantin Mishchenko; Peter Richtárik (CoRR, 2019)

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

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

Tighter Theory for Local SGD on Identical and Heterogeneous Data. - Ahmed Khaled 001; Konstantin Mishchenko; Peter Richtárik (AISTATS, 2020)

Revisiting Stochastic Extragradient. - Konstantin Mishchenko; Dmitry Kovalev; Egor Shulgin; Peter Richtárik; Yura Malitsky (AISTATS, 2020)

DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate. - Saeed Soori; Konstantin Mishchenko; Aryan Mokhtari; Maryam Mehri Dehnavi; Mert Gürbüzbalaban (AISTATS, 2020)

Adaptive Gradient Descent without Descent. - Yura Malitsky; Konstantin Mishchenko (ICML, 2020)

Random Reshuffling: Simple Analysis with Vast Improvements. - Konstantin Mishchenko; Ahmed Khaled 001; Peter Richtárik (NeurIPS, 2020)

99% of Worker-Master Communication in Distributed Optimization Is Not Needed. - Konstantin Mishchenko; Filip Hanzely; Peter Richtárik (UAI, 2020)

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

A Distributed Flexible Delay-Tolerant Proximal Gradient Algorithm. - Konstantin Mishchenko; Franck Iutzeler; Jérôme Malick (SIAM J. Optim., 2020)

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

Regularized Newton Method with Global O(1/k2) Convergence. - Konstantin Mishchenko (CoRR, 2021)

IntSGD: Adaptive Floatless Compression of Stochastic Gradients. - Konstantin Mishchenko; Bokun Wang; Dmitry Kovalev; Peter Richtárik (ICLR, 2022)

Proximal and Federated Random Reshuffling. - Konstantin Mishchenko; Ahmed Khaled 001; Peter Richtárik (ICML, 2022)

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

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

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

Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. - Adil Salim; Laurent Condat; Konstantin Mishchenko; Peter Richtárik (J. Optim. Theory Appl., 2022)

Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. - Grigory Malinovsky; Konstantin Mishchenko; Peter Richtárik (DistributedML@CoNEXT, 2023)

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

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

DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method. - Ahmed Khaled 001; Konstantin Mishchenko; Chi Jin 001 (NeurIPS, 2023)

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

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

Stochastic distributed learning with gradient quantization and double-variance reduction. - Samuel Horváth; Dmitry Kovalev; Konstantin Mishchenko; Peter Richtárik; Sebastian U. Stich (Optim. Methods Softw., 2023)

Regularized Newton Method with Global ({ boldsymbol{ mathcal{O}(1/{k}^2)}} ) Convergence. - Konstantin Mishchenko (SIAM J. Optim., 2023)

Prodigy: An Expeditiously Adaptive Parameter-Free Learner. - Konstantin Mishchenko; Aaron Defazio (ICML, 2024)

The Road Less Scheduled. - Aaron Defazio; Xingyu Yang; Ahmed Khaled 001; Konstantin Mishchenko; Harsh Mehta; Ashok Cutkosky (NeurIPS, 2024)

Adaptive Proximal Gradient Method for Convex Optimization. - Yura Malitsky; Konstantin Mishchenko (NeurIPS, 2024)

Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference. - Hao Mark Chen; Wayne Luk; Ka Fai Cedric Yiu; Rui Li 052; Konstantin Mishchenko; Stylianos I. Venieris; Hongxiang Fan (CoRR, 2024)

Super-Universal Regularized Newton Method. - Nikita Doikov; Konstantin Mishchenko; Yurii E. Nesterov (SIAM J. Optim., 2024)

Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity. - Konstantin Mishchenko; Rustem Islamov; Eduard Gorbunov; Samuel Horváth (Trans. Mach. Learn. Res., 2025)

Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation. - Robert M. Gower; Guillaume Garrigos; Nicolas Loizou; Dimitris Oikonomou; Konstantin Mishchenko; Fabian Schaipp (CoRR, 2025)