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Peter Richtárik

Active 2010–2026

296
Papers
19,109
Citations
63
h-index
186
i10-index

Citations

Citations per year for Peter Richtárik1971: 1 citations1995: 1 citations2010: 7 citations2011: 16 citations2012: 24 citations2013: 71 citations2014: 180 citations2015: 325 citations2016: 362 citations2017: 350 citations2018: 392 citations2019: 822 citations2020: 1,514 citations2021: 1,931 citations2022: 1,875 citations2023: 1,709 citations2024: 1,683 citations2025: 1,305 citations2026: 355 citations2027: 1 citations1972–1994: no citations, so these years are not shown1996–2009: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorChina: 2,407 citing papers, 23.3% of this breakdownUnited States: 2,317 citing papers, 22.4% of this breakdownUnited Kingdom: 587 citing papers, 5.7% of this breakdownHong Kong: 395 citing papers, 3.8% of this breakdownCanada: 385 citing papers, 3.7% of this breakdownAustralia: 327 citing papers, 3.2% of this breakdownFrance: 321 citing papers, 3.1% of this breakdownGermany: 293 citing papers, 2.8% of this breakdownSingapore: 249 citing papers, 2.4% of this breakdownSouth Korea: 247 citing papers, 2.4% of this breakdownIndia: 212 citing papers, 2% of this breakdownItaly: 180 citing papers, 1.7% of this breakdown
0%23.3%Other 23.5%

Institutions

Fields

  • Computer Science76.2%
  • Engineering14.2%
  • Mathematics2.5%
  • Medicine1.5%
  • Decision Sciences1.5%
  • Biochemistry, Genetics and Molecular Biology0.7%
  • Other3.4%

Topics

  • Privacy-Preserving Technologies in Data16.6%
  • Stochastic Gradient Optimization Techniques11.8%
  • Sparse and Compressive Sensing Techniques6.4%
  • Cryptography and Data Security3.9%
  • Mobile Crowdsensing and Crowdsourcing2.1%
  • IoT and Edge/Fog Computing1.8%
  • Other57.4%

Coauthors

All papers

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  1. Federated Learning: Strategies for Improving Communication Efficiency

    Authors: Jakub Konecný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon - arXiv (Cornell University), CoRR 2016 cited by 3,440

  2. Federated Optimization: Distributed Machine Learning for On-Device Intelligence

    Authors: Jakub Konecný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik - arXiv (Cornell University), CoRR 2016 cited by 1,703

  3. A Field Guide to Federated Optimization

    Authors: Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu - arXiv (Cornell University), CoRR 2021 cited by 378

  4. Federated Learning of a Mixture of Global and Local Models

    Authors: Filip Hanzely, Peter Richtárik - arXiv (Cornell University), CoRR 2020 cited by 326

  5. Randomized Iterative Methods for Linear Systems

    Authors: Robert Mansel Gower, Peter Richtárik - SIAM Journal on Matrix Analysis and Applications, SIAM J. Matrix Anal. Appl. 2015 cited by 198

  6. Optimal Client Sampling for Federated Learning

    Authors: Wenlin Chen, Samuel Horváth, Peter Richtárik - Trans. Mach. Learn. Res. 2022 cited by 141

  7. Distributed Learning with Compressed Gradient Differences

    Authors: Konstantin Mishchenko, Eduard Gorbunov, Martin Takác, Peter Richtárik - Optimization methods & software, Optim. Methods Softw. 2019 cited by 186

  8. Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs)

    Authors: Artem Riabinin, Egor Shulgin, Kaja Gruntkowska, Peter Richtárik - ArXiv.org, CoRR 2025 cited by 54

  9. Better Theory for SGD in the Nonconvex World

    Authors: Ahmed Khaled, Peter Richtárik - Trans. Mach. Learn. Res. 2023 cited by 112

  10. Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function

    Authors: Peter Richtárik, Martin Takác - Mathematical Programming, Math. Program. 2012 cited by 718

  11. Stochastic distributed learning with gradient quantization and double-variance reduction

    Authors: Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik, Sebastian U. Stich - Optimization methods & software, Optim. Methods Softw. 2022 cited by 46

  12. On Biased Compression for Distributed Learning

    Authors: Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan - J. Mach. Learn. Res. 2023 cited by 117

  13. openalex_id:w3088745370

    Authors: Nicolas Loizou, Peter Richtárik cited by 109

  14. Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications

    Authors: Antonin Chambolle, Matthias J. Ehrhardt, Peter Richtárik, Carola-Bibiane Schönlieb - SIAM Journal on Optimization, SIAM J. Optim. 2017 cited by 176

  15. Distributed Optimization with Arbitrary Local Solvers

    Authors: Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác - Optimization methods & software, Optim. Methods Softw. 2017 cited by 195

  16. First Analysis of Local GD on Heterogeneous Data

    Authors: Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik - arXiv (Cornell University), CoRR 2019 cited by 108

  17. EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback

    Authors: Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov, Zhize Li, Peter Richtárik - J. Mach. Learn. Res. 2025 cited by 54

  18. EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback

    Authors: Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin - NeurIPS 2021 cited by 210

  19. Parallel Coordinate Descent Methods for Big Data Optimization

    Authors: Peter Richtárik, Martin Takác - Mathematical Programming, Math. Program. 2015 cited by 374

  20. FedNL: Making Newton-Type Methods Applicable to Federated Learning

    Authors: Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtárik - ICML 2022 cited by 92

  21. Natural Compression for Distributed Deep Learning

    Authors: Samuel Horváth, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, Peter Richtárik - MSML 2022 cited by 103

  22. Scaling Distributed Machine Learning with In-Network Aggregation

    Authors: Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, Dan R. K. Ports, Peter Richtárik - NSDI 2021 cited by 123

  23. PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression

    Authors: Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, Peter Richtárik - Advances in Neural Information Processing Systems 37, NeurIPS 2024 cited by 56

  24. Methods for Convex (L0, L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity

    Authors: Eduard Gorbunov, Nazarii Tupitsa, Sayantan Choudhury, Alen Aliev, Peter Richtárik, Samuel Horváth, Martin Takác - ICLR 2025 cited by 20