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

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
I'm giving a talk at Institut Henri Poincaré
I’m giving a talk at Séminaire Parisien d’Optimisation on 10 October. I will be presenting my work on second-order optimization, including the Super-Universal Newton paper.
Last updated on Oct 10, 2022 1 min read
I'm attending LOL2022
This week I’m participating in the Learning and Optimization in Luminy (LOL2022) workshop. Yesterday I gave a short presentation of our Super-Universal Newton paper.
Last updated on Oct 4, 2022 1 min read
Asynchronous SGD accepted at NeurIPS
Our paper on Asynchronous SGD was accepted for publication at NeurIPS 2022. Hopefully, I’ll present the paper in person in December, so feel free to drop me a message if you’re planning to attend as well.
Last updated on Sep 19, 2022 1 min read
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Recent Papers

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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.

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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.

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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.

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.

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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.

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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.

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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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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.

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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.

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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.

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

PDF Cite arXiv SIAM

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