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Peter L. Bartlett

Active 1991–2026

265
Papers
34,279
Citations
72
h-index
192
i10-index

Citations

Citations per year for Peter L. Bartlett1905: 1 citations1976: 1 citations1979: 2 citations1981: 1 citations1992: 3 citations1993: 10 citations1994: 9 citations1995: 17 citations1996: 27 citations1997: 48 citations1998: 108 citations1999: 144 citations2000: 174 citations2001: 206 citations2002: 249 citations2003: 268 citations2004: 287 citations2005: 325 citations2006: 382 citations2007: 380 citations2008: 453 citations2009: 431 citations2010: 474 citations2011: 551 citations2012: 585 citations2013: 543 citations2014: 640 citations2015: 593 citations2016: 593 citations2017: 687 citations2018: 877 citations2019: 1,298 citations2020: 1,562 citations2021: 1,735 citations2022: 1,212 citations2023: 1,044 citations2024: 1,097 citations2025: 843 citations2026: 214 citations1906–1975: no citations, so these years are not shown1977–1978: no citations, so these years are not shown1980: no citations, so this year is not shown1982–1991: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 4,912 citing papers, 28.2% of this breakdownChina: 2,933 citing papers, 16.8% of this breakdownUnited Kingdom: 1,044 citing papers, 6% of this breakdownGermany: 765 citing papers, 4.4% of this breakdownFrance: 713 citing papers, 4.1% of this breakdownAustralia: 614 citing papers, 3.5% of this breakdownCanada: 609 citing papers, 3.5% of this breakdownJapan: 481 citing papers, 2.8% of this breakdownItaly: 442 citing papers, 2.5% of this breakdownIsrael: 426 citing papers, 2.5% of this breakdownIndia: 370 citing papers, 2.1% of this breakdownHong Kong: 350 citing papers, 2% of this breakdown
0%28.2%Other 21.6%

Institutions

Fields

  • Computer Science67.9%
  • Engineering11.1%
  • Decision Sciences6.1%
  • Mathematics4.6%
  • Biochemistry, Genetics and Molecular Biology1.7%
  • Physics and Astronomy1.6%
  • Other7%

Topics

  • Machine Learning and Algorithms5.4%
  • Face and Expression Recognition4.4%
  • Neural Networks and Applications4.2%
  • Reinforcement Learning in Robotics3.3%
  • Machine Learning and Data Classification3.2%
  • Sparse and Compressive Sensing Techniques3.2%
  • Other76.3%

Coauthors

All papers

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  1. Proceedings of the 24th International Conference on Neural Information Processing Systems

    Authors: John Shawe‐Taylor, Richard S. Zemel, Peter L. Bartlett, Francisco B. Pereira, Kilian Q. Weinberger - 2011 cited by 1,190

  2. RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning

    Authors: Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel - arXiv (Cornell University), CoRR 2016 cited by 767

  3. Trained Transformers Learn Linear Models In-Context

    Authors: Ruiqi Zhang, Spencer Frei, Peter L. Bartlett - J. Mach. Learn. Res. 2024 cited by 167

  4. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results

    Authors: Peter L. Bartlett, Shahar Mendelson - Lecture notes in computer science, COLT/EuroCOLT 2001 cited by 2,132

  5. New Support Vector Algorithms

    Authors: Bernhard Schölkopf, Alexander J. Smola, Robert C. Williamson, Peter L. Bartlett - Neural Computation, Neural Comput. 1998 cited by 2,816

  6. Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

    Authors: Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett - International Conference on Machine Learning, ICML 2018 cited by 2,286

  7. Convexity, Classification, and Risk Bounds

    Authors: Peter L. Bartlett, Michael I. Jordan, Jon McAuliffe - Journal of the American Statistical Association 2006 cited by 1,056

  8. Fast Best-of-N Decoding via Speculative Rejection

    Authors: Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao Qiu, Ming Yin, Mengdi Wang, Peter L. Bartlett, Andrea Zanette - NeurIPS 2024 cited by 138

  9. How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?

    Authors: Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett - ICLR 2024 cited by 102

  10. Infinite-Horizon Policy-Gradient Estimation

    Authors: Jonathan Baxter, Peter L. Bartlett - Journal of Artificial Intelligence Research, J. Artif. Intell. Res. 2001 cited by 539

  11. Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting

    Authors: Xiang Cheng, Niladri S. Chatterji, Yasin Abbasi-Yadkori, Peter L. Bartlett, Michael I. Jordan - CoRR 2018 cited by 110

  12. Neural Network Learning - Theoretical Foundations

    Authors: Martin Anthony, Peter L. Bartlett - Cambridge University Press eBooks 1999 cited by 1,365

  13. Benign overfitting in ridge regression

    Authors: Alexander Tsigler, Peter L. Bartlett - J. Mach. Learn. Res. 2023 cited by 105

  14. Local Rademacher Complexities

    Authors: Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson - The Annals of Statistics 2002 cited by 590

  15. Spectrally-normalized margin bounds for neural networks

    Authors: Peter L. Bartlett, Dylan J. Foster, Matus Telgarsky - NIPS 2017 cited by 1,473

  16. Self-Distillation Amplifies Regularization in Hilbert Space

    Authors: Hossein Mobahi, Mehrdad Farajtabar, Peter L. Bartlett - Neural Information Processing Systems, NeurIPS 2020 cited by 286

  17. Scaling Laws in Linear Regression: Compute, Parameters, and Data

    Authors: Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee - Advances in Neural Information Processing Systems 37, NeurIPS 2024 cited by 51

  18. The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network

    Authors: Peter L. Bartlett - IEEE Transactions on Information Theory, IEEE Trans. Inf. Theory 1998 cited by 1,194

  19. Deep learning: a statistical viewpoint

    Authors: Peter L. Bartlett, Andrea Montanari, Alexander Rakhlin - Acta Numerica, Acta Numer. 2021 cited by 70

  20. REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs

    Authors: Peter L. Bartlett, Ambuj Tewari - UAI 2009 cited by 148

  21. Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning

    Authors: Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, Nina Taft - Journal of Privacy and Confidentiality, J. Priv. Confidentiality 2012 cited by 250

  22. In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization

    Authors: Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett - Advances in Neural Information Processing Systems 37, NeurIPS 2024 cited by 37

  23. Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data

    Authors: Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu - ICLR 2023 cited by 52

  24. Regret Bound Balancing and Elimination for Model Selection in Bandits and RL

    Authors: Aldo Pacchiano, Christoph Dann, Claudio Gentile, Peter L. Bartlett - arXiv (Cornell University), CoRR 2020 cited by 45