Konstantin Mishchenko

Konstantin Mishchenko

Research Scientist

On a break

Bio

Hi there, I’m Konstantin, a researcher and wannabe musician. I like using mathematics to make things work in practice, especially in deep learning applications. Previously I was a research scientist at Samsung AI Center in Cambridge, UK. Beside doing research, I serve as an Action Editor for TMLR, tweet about interesting papers, and give talks about my studies. In 2023, I was lucky to receive the Outsdanding Paper Award together with Aaron Defazio for our work on adaptive methods.

Before joining Samsung, I did a postdoc at Inria Sierra with Alexandre d’Aspremont and Francis Bach. I received my PhD from KAUST, where I worked under the supervision of Peter Richtárik on optimization theory and its applications in machine learning. In 2020, I also interned at Google Brain. 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 interests and hobbies tend to change every couple of years or so. Recently, I finished 6 months of evening classes at The Institute of Contemporary Music Performance where I studied electronic music production using Ableton Live. I hope to release some music online in the future.

Feel free to shoot me an email if you want to chat in person about research or music, go to a museum, or maybe just take a walk in Paris!

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 – Oct 2024 Cambridge, UK
Working on embedded AI systems as a member of the Distributed AI team and GenAI initiative. Some of the things I worked on: Non-autoregressive multi-token generation for LLMs using soft prompt tuning (paper under review); efficient transformer layers for on-device models; federated learning with streaming clients using small batch size (submitted a patent); federated learning under heterogeneous data (paper under review); adaptive optimization methods for automated training (papers published at ICML 2023 and ICML 2024).
 
 
 
 
 
Inria Sierra
Postdoc
Dec 2021 – Dec 2022 Paris, France
Conducted research on adaptive, second-order, and distributed optimization.

Recent Posts

Recent Papers

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(2024). Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference.

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(2024). The Road Less Scheduled.

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(2023). When, Why and How Much? Adaptive Learning Rate Scheduling by Refinement.

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(2023). Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity.

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(2023). Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy.

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(2023). Learning-Rate-Free Learning by D-Adaptation.

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(2023). Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes.

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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.

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(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.

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

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

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

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

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

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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