Konstantin Mishchenko

Konstantin Mishchenko

Research Scientist

Samsung

Bio

I’m a research scientist at Samsung AI Center in Cambridge, UK. Before joining Samsung, I did a postdoc at Inria Sierra with Alexandre d’Aspremont and Francis Bach. I received my PhD in computer science from KAUST, where I worked under the supervision of Peter Richtárik on optimization theory and its applications in machine learning. In 2020, I interned at Google Brain hosted by Nicolas Le Roux and Courtney Paquette. Prior to that, I obtained my double degree MSc diploma from École Normale Supérieure Paris-Saclay and Paris-Dauphine, and a BSc from Moscow Institute of Physics and Technology.

My hobbies include ultimate frisbee, squash and bouldering.

Note that I’m currently not taking any interns.

Interests
  • Optimization
  • Deep learning
  • Federated and distributed learning
Education

  • PhD in Computer Science, 2021

    KAUST

  • MSc in Data Science, 2017

    École normale supérieure Paris-Saclay and Paris-Dauphine

  • BSc in Computer Science and Physics, 2016

    Moscow Institute of Physics and Technology

Experience

 
 
 
 
 
Samsung
Research Scientist
Samsung
Jan 2023 – Present Cambridge, UK
Working on federated learning and embedded AI systems as a member of the Distributed AI team.
 
 
 
 
 
Inria Sierra
Postdoc
Inria Sierra
Dec 2021 – Dec 2022 Paris, France
Conducted research on adaptive, second-order, and distributed optimization.

Recent Posts

2 papers accepted at ICML (oral and poster)
Two of my papers got accepted to ICML 2023. Our work on two losses with B. Woodworth and F. Bach will be presented as a poster and our paper on D-Adaptation with A. Defazio got selected as an oral presentation!
Last updated on Apr 25, 2023 1 min read
Online talk at Technology Innovation Institute
Today I’m giving an online talk at AIDRC Seminar Series of Technology Innovation Institute. The talk announcement can be found on the seminar’s website along with the abstract. In short, The topic of my presentation is our 2022 ICML paper ProxSkip and its several extension that were done by other authors.
Last updated on Mar 30, 2023 1 min read
Paper on Regularized Newton accepted at SIAM Journal on Optimization (SIOPT)
My paper on Regularized Newton got accepted for publication at SIAM Journal on Optimization (SIOPT). The main result of this work is to show that one can globalize Newton’s method by using regularization proportional to the square root of the gradient norm. The corresponding method achieves global acceleration over gradient descent and it converges with the $O(1/k^2)$ rate of cubic Newton.
Last updated on Feb 20, 2023 1 min read
First day at Samsung
Today I’m starting in my new role as a Research Scientist at Samsung AI Center in Cambridge, UK. I’ll be part of the Distributed AI team and work on federated learning and distributed optimization.
Last updated on Jan 12, 2023 1 min read
Presenting at 2022 Workshop on FL and Analytics organized by Google
I’m taking part in the 2022 Workshop on Federated Learning and Analytics on 9 and 10 November. I am giving a talk about our work on Asynchronous SGD in the mini-workshop on Federated Systems at Scale on 9 November.
Last updated on Nov 10, 2022 1 min read
See all posts

Recent Papers

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

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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.

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

PDF Cite Slides arXiv

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

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

PDF Cite Slides arXiv

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

PDF Cite Video arXiv ICML

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

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

PDF Cite Code Slides Video arXiv

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

PDF Cite Code Poster Slides arXiv ICLR

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

PDF Cite Code Slides Video arXiv ICML

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

PDF Cite Code Poster Slides arXiv NeurIPS

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

PDF Cite Poster arXiv JOTA

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

PDF Cite Poster arXiv NeurIPS

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

PDF Cite Code Poster Slides arXiv ICML Video

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

PDF Cite Poster Slides arXiv NeurIPS

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

PDF Cite Slides arXiv NeurIPS

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

PDF Cite Slides arXiv AISTATS

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

PDF Cite arXiv ICML

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

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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.

PDF Cite arXiv AISTATS

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

PDF Cite Slides arXiv

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

PDF Cite Slides arXiv AISTATS

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.

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

PDF Cite arXiv UAI

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

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

PDF Cite Poster arXiv

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

PDF Cite arXiv NIPS

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

PDF Cite ICML

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

PDF Cite arXiv SIAM

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