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ORCIDKonstantin Mishchenko
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2020 – today
- 2024
[j5]Nikita Doikov
, Konstantin Mishchenko, Yurii E. Nesterov:
Super-Universal Regularized Newton Method. SIAM J. Optim. 34(1): 27-56 (2024)- [c17]Konstantin Mishchenko, Aaron Defazio:
Prodigy: An Expeditiously Adaptive Parameter-Free Learner. ICML 2024 - [i30]Aaron Defazio, Xingyu Yang, Harsh Mehta, Konstantin Mishchenko, Ahmed Khaled, Ashok Cutkosky:
The Road Less Scheduled. CoRR abs/2405.15682 (2024) - [i29]Hao Mark Chen, Wayne Luk, Ka Fai Cedric Yiu, Rui Li, Konstantin Mishchenko, Stylianos I. Venieris, Hongxiang Fan:
Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference. CoRR abs/2405.18628 (2024) - 2023
- [j4]Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik, Sebastian U. Stich:
Stochastic distributed learning with gradient quantization and double-variance reduction. Optim. Methods Softw. 38(1): 91-106 (2023) - [j3]Konstantin Mishchenko:
Regularized Newton Method with Global \({\boldsymbol{\mathcal{O}(1/{k}^2)}}\) Convergence. SIAM J. Optim. 33(3): 1440-1462 (2023) - [c16]Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik:
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. DistributedML@CoNEXT 2023: 85-104 - [c15]Aaron Defazio, Konstantin Mishchenko:
Learning-Rate-Free Learning by D-Adaptation. ICML 2023: 7449-7479 - [c14]Blake E. Woodworth, Konstantin Mishchenko, Francis R. Bach:
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy. ICML 2023: 37273-37292 - [c13]Ahmed Khaled, Konstantin Mishchenko, Chi Jin:
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method. NeurIPS 2023 - [i28]Konstantin Mishchenko, Slavomír Hanzely, Peter Richtárik:
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes. CoRR abs/2301.06806 (2023) - [i27]Aaron Defazio, Konstantin Mishchenko:
Learning-Rate-Free Learning by D-Adaptation. CoRR abs/2301.07733 (2023) - [i26]Blake E. Woodworth, Konstantin Mishchenko, Francis R. Bach:
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy. CoRR abs/2302.03542 (2023) - [i25]Ahmed Khaled, Konstantin Mishchenko, Chi Jin:
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method. CoRR abs/2305.16284 (2023) - [i24]Konstantin Mishchenko, Rustem Islamov, Eduard Gorbunov, Samuel Horváth:
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity. CoRR abs/2305.18285 (2023) - [i23]Konstantin Mishchenko, Aaron Defazio:
Prodigy: An Expeditiously Adaptive Parameter-Free Learner. CoRR abs/2306.06101 (2023) - [i22]Yura Malitsky, Konstantin Mishchenko:
Adaptive Proximal Gradient Method for Convex Optimization. CoRR abs/2308.02261 (2023) - [i21]Aaron Defazio, Ashok Cutkosky, Harsh Mehta, Konstantin Mishchenko:
When, Why and How Much? Adaptive Learning Rate Scheduling by Refinement. CoRR abs/2310.07831 (2023) - 2022
- [j2]Adil Salim, Laurent Condat, Konstantin Mishchenko, Peter Richtárik:
Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. J. Optim. Theory Appl. 195(1): 102-130 (2022) - [c12]Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtárik:
IntSGD: Adaptive Floatless Compression of Stochastic Gradients. ICLR 2022 - [c11]Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik:
Proximal and Federated Random Reshuffling. ICML 2022: 15718-15749 - [c10]Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik:
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! ICML 2022: 15750-15769 - [c9]Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth:
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. NeurIPS 2022 - [i20]Grigory Malinovsky, Konstantin Mishchenko, Peter Richtárik:
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. CoRR abs/2201.11066 (2022) - [i19]Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik:
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! CoRR abs/2202.09357 (2022) - [i18]Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth:
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. CoRR abs/2206.07638 (2022) - [i17]Samuel Horváth, Konstantin Mishchenko, Peter Richtárik:
Adaptive Learning Rates for Faster Stochastic Gradient Methods. CoRR abs/2208.05287 (2022) - [i16]Nikita Doikov, Konstantin Mishchenko, Yurii E. Nesterov:
Super-Universal Regularized Newton Method. CoRR abs/2208.05888 (2022) - 2021
- [i15]Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik:
Proximal and Federated Random Reshuffling. CoRR abs/2102.06704 (2021) - [i14]Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtárik:
IntSGD: Floatless Compression of Stochastic Gradients. CoRR abs/2102.08374 (2021) - [i13]Konstantin Mishchenko:
Regularized Newton Method with Global O(1/k2) Convergence. CoRR abs/2112.02089 (2021) - 2020
- [j1]Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick:
A Distributed Flexible Delay-Tolerant Proximal Gradient Algorithm. SIAM J. Optim. 30(1): 933-959 (2020) - [c8]Saeed Soori, Konstantin Mishchenko, Aryan Mokhtari, Maryam Mehri Dehnavi, Mert Gürbüzbalaban:
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate. AISTATS 2020: 1965-1976 - [c7]Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik:
Tighter Theory for Local SGD on Identical and Heterogeneous Data. AISTATS 2020: 4519-4529 - [c6]Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky:
Revisiting Stochastic Extragradient. AISTATS 2020: 4573-4582 - [c5]Yura Malitsky, Konstantin Mishchenko:
Adaptive Gradient Descent without Descent. ICML 2020: 6702-6712 - [c4]Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik:
Random Reshuffling: Simple Analysis with Vast Improvements. NeurIPS 2020 - [c3]Konstantin Mishchenko, Filip Hanzely, Peter Richtárik:
99% of Worker-Master Communication in Distributed Optimization Is Not Needed. UAI 2020: 979-988 - [i12]Adil Salim, Laurent Condat, Konstantin Mishchenko, Peter Richtárik:
Dualize, Split, Randomize: Fast Nonsmooth Optimization Algorithms. CoRR abs/2004.02635 (2020) - [i11]Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik:
Random Reshuffling: Simple Analysis with Vast Improvements. CoRR abs/2006.05988 (2020)
2010 – 2019
- 2019
- [i10]Konstantin Mishchenko, Eduard Gorbunov, Martin Takác, Peter Richtárik:
Distributed Learning with Compressed Gradient Differences. CoRR abs/1901.09269 (2019) - [i9]Konstantin Mishchenko, Filip Hanzely, Peter Richtárik:
99% of Parallel Optimization is Inevitably a Waste of Time. CoRR abs/1901.09437 (2019) - [i8]Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky:
Revisiting Stochastic Extragradient. CoRR abs/1905.11373 (2019) - [i7]Konstantin Mishchenko, Mallory Montgomery, Federico Vaggi:
A Self-supervised Approach to Hierarchical Forecasting with Applications to Groupwise Synthetic Controls. CoRR abs/1906.10586 (2019) - [i6]Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik:
First Analysis of Local GD on Heterogeneous Data. CoRR abs/1909.04715 (2019) - [i5]Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik:
Better Communication Complexity for Local SGD. CoRR abs/1909.04746 (2019) - [i4]Konstantin Mishchenko:
Sinkhorn Algorithm as a Special Case of Stochastic Mirror Descent. CoRR abs/1909.06918 (2019) - [i3]Yura Malitsky, Konstantin Mishchenko:
Adaptive gradient descent without descent. CoRR abs/1910.09529 (2019) - [i2]Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik:
Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates. CoRR abs/1912.01597 (2019) - 2018
- [c2]Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick, Massih-Reza Amini:
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning. ICML 2018: 3584-3592 - [c1]Filip Hanzely, Konstantin Mishchenko, Peter Richtárik:
SEGA: Variance Reduction via Gradient Sketching. NeurIPS 2018: 2086-2097 - [i1]Filip Hanzely, Konstantin Mishchenko, Peter Richtárik:
SEGA: Variance Reduction via Gradient Sketching. CoRR abs/1809.03054 (2018)
Coauthor Index
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last updated on 2024-09-04 01:20 CEST by the dblp team
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