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Michael J. Kearns

Active 1985–2023

163
Papers
18,483
Citations
63
h-index
128
i10-index

Citations

Citations per year for Michael J. Kearns1957: 2 citations1960: 1 citations1976: 1 citations1979: 1 citations1985: 1 citations1987: 11 citations1988: 33 citations1989: 64 citations1990: 52 citations1991: 55 citations1992: 60 citations1993: 72 citations1994: 97 citations1995: 166 citations1996: 130 citations1997: 120 citations1998: 155 citations1999: 180 citations2000: 166 citations2001: 182 citations2002: 188 citations2003: 239 citations2004: 212 citations2005: 283 citations2006: 373 citations2007: 342 citations2008: 385 citations2009: 369 citations2010: 359 citations2011: 366 citations2012: 359 citations2013: 369 citations2014: 400 citations2015: 421 citations2016: 367 citations2017: 439 citations2018: 528 citations2019: 631 citations2020: 765 citations2021: 787 citations2022: 472 citations2023: 418 citations2024: 342 citations2025: 250 citations2026: 74 citations1958–1959: no citations, so these years are not shown1961–1975: no citations, so these years are not shown1977–1978: no citations, so these years are not shown1980–1984: no citations, so these years are not shown1986: no citations, so this year is not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 4,186 citing papers, 38.5% of this breakdownUnited Kingdom: 766 citing papers, 7% of this breakdownChina: 712 citing papers, 6.5% of this breakdownGermany: 589 citing papers, 5.4% of this breakdownCanada: 542 citing papers, 5% of this breakdownIsrael: 519 citing papers, 4.8% of this breakdownFrance: 441 citing papers, 4% of this breakdownJapan: 306 citing papers, 2.8% of this breakdownItaly: 268 citing papers, 2.5% of this breakdownNetherlands: 221 citing papers, 2% of this breakdownAustralia: 208 citing papers, 1.9% of this breakdownSpain: 180 citing papers, 1.7% of this breakdown
0%38.5%Other 17.9%

Institutions

Fields

  • Computer Science67.1%
  • Decision Sciences11.8%
  • Social Sciences5.5%
  • Engineering3.7%
  • Physics and Astronomy3.2%
  • Economics, Econometrics and Finance2.1%
  • Other6.6%

Topics

  • Machine Learning and Algorithms9.7%
  • Reinforcement Learning in Robotics4.7%
  • Algorithms and Data Compression3%
  • Machine Learning and Data Classification3%
  • Advanced Bandit Algorithms Research2.8%
  • Game Theory and Applications2.8%
  • Other74%

Coauthors

All papers

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  1. A Convex Framework for Fair Regression

    Authors: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth - arXiv (Cornell University), CoRR 2017 cited by 253

  2. An Introduction to Computational Learning Theory

    Authors: Michael J. Kearns, Umesh V. Vazirani - The MIT Press eBooks 1994 cited by 1,733

  3. Efficient Noise-Tolerant Learning from Statistical Queries

    Authors: Michael J. Kearns - Journal of the ACM, J. ACM 1993 cited by 712

  4. Cryptographic Primitives Based on Hard Learning Problems

    Authors: Avrim Blum, Merrick L. Furst, Michael J. Kearns, Richard J. Lipton - Lecture notes in computer science, CRYPTO 1993 cited by 340

  5. Near-Optimal Reinforcement Learning in Polynomial Time

    Authors: Michael J. Kearns, Satinder Singh - Machine Learning, Mach. Learn. 1998 cited by 858

  6. Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

    Authors: Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu - International Conference on Machine Learning, ICML 2017 cited by 919

  7. STING agonism overcomes STAT3-mediated immunosuppression and adaptive resistance to PARP inhibition in ovarian cancer

    Authors: Liya Ding, Qiwei Wang, Antons Martincuks, Michael J. Kearns, Tao Jiang, Ziying Lin, Xin Cheng, Changli Qian, Shaozhen Xie, Hye‐Jung Kim, Inga-Maria Launonen, Anniina Färkkilä, Thomas M. Roberts, Gordon J. Freeman, Joyce F. Liu, Panagiotis A. Konstantinopoulos, Ursula A. Matulonis, Hua Yu, Jean J Zhao - Journal for ImmunoTherapy of Cancer 2023 cited by 92

  8. Reinforcement learning for optimized trade execution

    Authors: Yuriy Nevmyvaka, Yi Feng, Michael J. Kearns - conference on Machine learning - ICML '06 2006 cited by 262

  9. Cryptographic Limitations on Learning Boolean Formulae and Finite Automata

    Authors: Michael J. Kearns, Leslie G. Valiant - Journal of the ACM, J. ACM 1989 cited by 744

  10. Fairness in Learning: Classic and Contextual Bandits

    Authors: Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth - Neural Information Processing Systems, NIPS 2016 cited by 515

  11. Toward Efficient Agnostic Learning

    Authors: Michael J. Kearns, Robert E. Schapire, Linda Sellie - Machine Learning, COLT 1992 cited by 349

  12. A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes

    Authors: Michael J. Kearns, Yishay Mansour, Andrew Y. Ng - Machine Learning, IJCAI 1999 cited by 677

  13. Weakly learning DNF and characterizing statistical query learning using Fourier analysis

    Authors: Avrim Blum, Merrick L. Furst, Jeffrey C. Jackson, Michael J. Kearns, Yishay Mansour, Steven Rudich - twenty-sixth annual ACM symposium on Theory of computing - STOC '94 1994 cited by 264

  14. Proceedings 6th ACM Conference on Electronic Commerce (EC-2005), Vancouver, BC, Canada, June 5-8, 2005

    Authors: John Riedl, Michael J. Kearns, Michael K. Reiter - EC 2005 cited by 107

  15. On the Complexity of Teaching

    Authors: Sally A. Goldman, Michael J. Kearns - Conference on Learning Theory, COLT 1991 cited by 277

  16. Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation

    Authors: Michael J. Kearns, Dana Ron - Neural Computation, COLT 1997 cited by 418

  17. Efficient Distribution-Free Learning of Probabilistic Concepts

    Authors: Michael J. Kearns, Robert E. Schapire - Elsevier eBooks, J. Comput. Syst. Sci. 1990 cited by 377

  18. On the learnability of discrete distributions

    Authors: Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie - twenty-sixth annual ACM symposium on Theory of computing - STOC '94 1994 cited by 287

  19. An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering

    Authors: Michael J. Kearns, Yishay Mansour, Andrew Y. Ng - Learning in Graphical Models 1997 cited by 176

  20. Fair Algorithms for Learning in Allocation Problems

    Authors: Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman - Conference on Fairness, FAT 2019 cited by 69

  21. Market making and mean reversion

    Authors: Tanmoy Chakraborty, Michael J. Kearns - conference on Electronic commerce 2011 cited by 59

  22. An Empirical Study of Rich Subgroup Fairness for Machine Learning

    Authors: Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu - Conference on Fairness, FAT 2019 cited by 33

  23. Graphical Models for Game Theory

    Authors: Michael J. Kearns, Michael L. Littman, Satinder Singh - UAI 2001 cited by 574

  24. Eliciting and Enforcing Subjective Individual Fairness

    Authors: Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu - FORC 2021 cited by 53