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Peter Richtárik

2010–2026 年に発表

296
論文数
19,109
被引用数
63
h 指数
186
i10 指数

被引用数

Peter Richtárik の年別被引用数1971 年: 被引用 1 件1995 年: 被引用 1 件2010 年: 被引用 7 件2011 年: 被引用 16 件2012 年: 被引用 24 件2013 年: 被引用 71 件2014 年: 被引用 180 件2015 年: 被引用 325 件2016 年: 被引用 362 件2017 年: 被引用 350 件2018 年: 被引用 392 件2019 年: 被引用 822 件2020 年: 被引用 1,514 件2021 年: 被引用 1,931 件2022 年: 被引用 1,875 件2023 年: 被引用 1,709 件2024 年: 被引用 1,683 件2025 年: 被引用 1,305 件2026 年: 被引用 355 件2027 年: 被引用 1 件1972〜1994 年は被引用が無いため表示していません1996〜2009 年は被引用が無いため表示していません

引用元

国・地域

この著者を引用した国・地域の世界地図中国: 引用元論文 2,407 件、この内訳の 23.3%アメリカ合衆国: 引用元論文 2,317 件、この内訳の 22.4%イギリス: 引用元論文 587 件、この内訳の 5.7%香港: 引用元論文 395 件、この内訳の 3.8%カナダ: 引用元論文 385 件、この内訳の 3.7%オーストラリア: 引用元論文 327 件、この内訳の 3.2%フランス: 引用元論文 321 件、この内訳の 3.1%ドイツ: 引用元論文 293 件、この内訳の 2.8%シンガポール: 引用元論文 249 件、この内訳の 2.4%韓国: 引用元論文 247 件、この内訳の 2.4%インド: 引用元論文 212 件、この内訳の 2%イタリア: 引用元論文 180 件、この内訳の 1.7%
0%23.3%その他 23.5%

機関

分野

  • Computer Science76.2%
  • Engineering14.2%
  • Mathematics2.5%
  • Medicine1.5%
  • Decision Sciences1.5%
  • Biochemistry, Genetics and Molecular Biology0.7%
  • その他3.4%

トピック

  • 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%
  • その他57.4%

共著者

全論文

検索で開く
  1. Federated Learning: Strategies for Improving Communication Efficiency

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

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

    著者: Jakub Konecný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik - arXiv (Cornell University), CoRR 2016 被引用: 1,703

  3. A Field Guide to Federated Optimization

    著者: 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 被引用: 378

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

    著者: Filip Hanzely, Peter Richtárik - arXiv (Cornell University), CoRR 2020 被引用: 326

  5. Randomized Iterative Methods for Linear Systems

    著者: Robert Mansel Gower, Peter Richtárik - SIAM Journal on Matrix Analysis and Applications, SIAM J. Matrix Anal. Appl. 2015 被引用: 198

  6. Optimal Client Sampling for Federated Learning

    著者: Wenlin Chen, Samuel Horváth, Peter Richtárik - Trans. Mach. Learn. Res. 2022 被引用: 141

  7. Distributed Learning with Compressed Gradient Differences

    著者: Konstantin Mishchenko, Eduard Gorbunov, Martin Takác, Peter Richtárik - Optimization methods & software, Optim. Methods Softw. 2019 被引用: 186

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

    著者: Artem Riabinin, Egor Shulgin, Kaja Gruntkowska, Peter Richtárik - ArXiv.org, CoRR 2025 被引用: 54

  9. Better Theory for SGD in the Nonconvex World

    著者: Ahmed Khaled, Peter Richtárik - Trans. Mach. Learn. Res. 2023 被引用: 112

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

    著者: Peter Richtárik, Martin Takác - Mathematical Programming, Math. Program. 2012 被引用: 718

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

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

  12. On Biased Compression for Distributed Learning

    著者: Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan - J. Mach. Learn. Res. 2023 被引用: 117

  13. openalex_id:w3088745370

    著者: Nicolas Loizou, Peter Richtárik 被引用: 109

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

    著者: Antonin Chambolle, Matthias J. Ehrhardt, Peter Richtárik, Carola-Bibiane Schönlieb - SIAM Journal on Optimization, SIAM J. Optim. 2017 被引用: 176

  15. Distributed Optimization with Arbitrary Local Solvers

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

  16. First Analysis of Local GD on Heterogeneous Data

    著者: Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik - arXiv (Cornell University), CoRR 2019 被引用: 108

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

    著者: Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov, Zhize Li, Peter Richtárik - J. Mach. Learn. Res. 2025 被引用: 54

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

    著者: Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin - NeurIPS 2021 被引用: 210

  19. Parallel Coordinate Descent Methods for Big Data Optimization

    著者: Peter Richtárik, Martin Takác - Mathematical Programming, Math. Program. 2015 被引用: 374

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

    著者: Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtárik - ICML 2022 被引用: 92

  21. Natural Compression for Distributed Deep Learning

    著者: Samuel Horváth, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, Peter Richtárik - MSML 2022 被引用: 103

  22. Scaling Distributed Machine Learning with In-Network Aggregation

    著者: 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 被引用: 123

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

    著者: 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 被引用: 56

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

    著者: Eduard Gorbunov, Nazarii Tupitsa, Sayantan Choudhury, Alen Aliev, Peter Richtárik, Samuel Horváth, Martin Takác - ICLR 2025 被引用: 20