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 Papers

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(2022). Super-Universal Regularized Newton Method.

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

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

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(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.

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

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(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.

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

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

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

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(2019). Revisiting Stochastic Extragradient. AISTATS.

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(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.

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(2019). Distributed Learning with Compressed Gradient Differences.

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

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

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

PDF Cite arXiv SIAM