Konstantin Mishchenko

Konstantin Mishchenko

Postdoctoral Researcher

Inria Sierra

Bio

I’m a postdoc at Inria Sierra working 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 squash, ultimate frisbee, and bouldering.

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

 
 
 
 
 
Inria Sierra
Postdoc
Inria Sierra
Dec 2021 – Present Paris, France

Research directions:

  • Adaptive algorithms
  • Second-order algorithms
  • Distributed training

Recent Posts

Visiting Sebastian Stich in Germany
Between 22 June and 28 June, I visited Sebastian Stich, who works at CISPA Helmholtz Center for Information Security near Saarbrücken in Germany. We discusses new ways to solve nonconvex problems, and we already have some interesting results. Sebastian is also looking for PhD students, so if you or someone you know is looking for a good supervisor working on optimization, Sebastian should be the first person to contact!
New paper: Asynchronous SGD with arbitrary delays
My first ever optimization project was an ICML paper about an asynchronous gradient method. At the time, I was quite confused by the fact that no matter what I was doing, Asynchronous gradient descent still converged. Five years later, I can finally give an answer: Because Asynchronous SGD doesn’t care about the delays, which we proved in https://arxiv.org/abs/2206.07638 our new paper. For a short summary, you can read my twitter thread about the paper or check my slides.
I'm at attending SICO conference 12-15 June

In a few days, I am travelling to Autrans, Vercors near Grenoble for the SICO conference dedicated to the 60th birthday of Anatoli Juditsky. The conference will feature a number of speakers working on optimization and statistics. As I did my master’s thesis at the University of Grenoble, I’m really happy to go there again after having been away for almost 5 years.

On the last day of the conference, I will give a talk about a new paper on Asynchronous SGD. The work that I will present is also going to appear online quite soon.

Recent Papers

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(2022). Super-Universal Regularized Newton Method.

PDF Cite arXiv

(2022). Adaptive Learning Rates for Faster Stochastic Gradient Methods.

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(2022). Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays.

PDF Cite Slides arXiv

(2022). ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!. ICML.

PDF Cite Video arXiv ICML

(2022). Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization.

PDF Cite arXiv

(2021). IntSGD: Adaptive Floatless Compression of Stochastic Gradients. ICLR.

PDF Cite Code Poster Slides arXiv ICLR

(2021). Proximal and Federated Random Reshuffling. ICML.

PDF Cite Code Slides Video arXiv ICML

(2020). Random Reshuffling: Simple Analysis with Vast Improvements. NeurIPS.

PDF Cite Code Poster Slides arXiv NeurIPS

(2020). Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. JOTA.

PDF Cite Poster arXiv JOTA

(2019). Adaptive Gradient Descent without Descent. ICML.

PDF Cite Code Poster Slides arXiv ICML Video

(2019). Tighter Theory for Local SGD on Identical and Heterogeneous Data. AISTATS.

PDF Cite Slides arXiv AISTATS

(2019). First Analysis of Local GD on Heterogeneous Data.

PDF Cite Slides arXiv NeurIPS

(2019). MISO is Making a Comeback With Better Proofs and Rates.

PDF Cite arXiv

(2019). DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate. AISTATS.

PDF Cite arXiv AISTATS

(2019). Revisiting Stochastic Extragradient. AISTATS.

PDF Cite Slides arXiv AISTATS

(2019). Stochastic Distributed Learning with Gradient Quantization and Double Variance Reduction. Optimization Methods and Software.

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(2019). 99% of Worker-Master Communication in Distributed Optimization Is Not Needed. UAI.

PDF Cite arXiv UAI

(2019). Distributed Learning with Compressed Gradient Differences.

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(2018). SEGA: Variance Reduction via Gradient Sketching. NeurIPS.

PDF Cite arXiv NIPS

(2018). A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning. ICML.

PDF Cite ICML

(2018). A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm. SIOPT.

PDF Cite arXiv SIAM