World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
5291
World Ranking
8981
National Ranking
3814

Mathematics

D-Index
41
Citations
5355
World Ranking
1955
National Ranking
827

Research.com Recognitions

  • 2017 - Fellow of Alfred P. Sloan Foundation

Overview

Daniel M. Kane is affiliated with the University of California, San Diego in the United States. Their primary field of research is Computer Science, with a focus on various subfields such as Artificial Intelligence, Statistics and Probability, Computational Mechanics, Computational Theory and Mathematics, and Computer Vision and Pattern Recognition.

The main topics of Kane's research include Machine Learning and Algorithms, Statistical Methods and Inference, Machine Learning and Data Classification, Sparse and Compressive Sensing Techniques, Bayesian Modeling and Causal Inference, Advanced Statistical Methods and Models, and Complexity and Algorithms in Graphs.

Frequent coauthors in Kane's publications are:

  • Ilias Diakonikolas
  • Nikos Zarifis
  • Thanasis Pittas
  • Ankit Pensia
  • Sihan Liu

The researcher's publications appear predominantly in the following venues:

  • arXiv (Cornell University)
  • IEEE Transactions on Information Theory
  • Journal of Fluid Mechanics
  • 2021 ASEE Virtual Annual Conference Content Access Proceedings
  • Communications of the ACM

Notable recent papers authored by or coauthored with Kane include:

  • vqSGD: Vector Quantized Stochastic Gradient Descent, 2022, IEEE Transactions on Information Theory
  • Robustly Learning any Clusterable Mixture of Gaussians, 2020, arXiv (Cornell University)
  • Outlier Robust Mean Estimation with Subgaussian Rates via Stability, 2020, arXiv (Cornell University)
  • Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks, 2020, arXiv (Cornell University)
  • Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals, 2020, arXiv (Cornell University)

Kane has contributed to book publications with:

  • Algorithmic High-Dimensional Robust Statistics published by Cambridge University Press in 2023
  • The William Lowell Putnam Mathematical Competition 2001-2016, published by Serbian Mathematical Society in 2020

Daniel M. Kane received the Fellowship from the Alfred P. Sloan Foundation in 2017.

Best Publications

  • An optimal algorithm for the distinct elements problem

    Daniel M. Kane;Jelani Nelson;David P. Woodruff

  • Sparser Johnson-Lindenstrauss Transforms

    Daniel M. Kane;Jelani Nelson

  • Sever: A Robust Meta-Algorithm for Stochastic Optimization

    Ilias Diakonikolas;Gautam Kamath;Daniel M. Kane;Jerry Li

  • Robust Estimators in High-Dimensions Without the Computational Intractability

    Ilias Diakonikolas;Gautam Kamath;Daniel Kane;Jerry Li

  • Robust Estimators in High Dimensions without the Computational Intractability

    Ilias Diakonikolas;Gautam Kamath;Daniel M. Kane;Jerry Li

  • Statistical Query Lower Bounds for Robust Estimation of High-Dimensional Gaussians and Gaussian Mixtures

    Ilias Diakonikolas;Daniel M. Kane;Alistair Stewart

  • Being Robust (in High Dimensions) Can Be Practical

    Ilias Diakonikolas;Gautam Kamath;Daniel M. Kane;Jerry Li

  • On the exact space complexity of sketching and streaming small norms

    Daniel M. Kane;Jelani Nelson;David P. Woodruff

  • A New Approach for Testing Properties of Discrete Distributions

    Ilias Diakonikolas;Daniel M. Kane

  • Recent Advances in Algorithmic High-Dimensional Robust Statistics.

    Ilias Diakonikolas;Daniel M. Kane

  • Mass-surveillance without the State: Strongly Undetectable Algorithm-Substitution Attacks.

    Mihir Bellare;Joseph Jaeger;Daniel Kane

  • Modeling the distribution of ranks, Selmer groups, and Shafarevich–Tate groups of elliptic curves

    Manjul Bhargava;Daniel M. Kane;Hendrik W. Lenstra;Bjorn Poonen

  • Robustly learning a gaussian: getting optimal error, efficiently

    Ilias Diakonikolas;Gautam Kamath;Daniel M. Kane;Jerry Li

  • On the complexity of two-player win-lose games

    T. Abbott;D. Kane;P. Valiant

  • List-decodable robust mean estimation and learning mixtures of spherical gaussians

    Ilias Diakonikolas;Daniel M. Kane;Alistair Stewart

  • Bounded Independence Fools Degree-2 Threshold Functions

    Ilias Diakonikolas;Daniel M. Kane;Jelani Nelson

  • Fast moment estimation in data streams in optimal space

    Daniel M. Kane;Jelani Nelson;Ely Porat;David P. Woodruff

  • Testing identity of structured distributions

    Ilias Diakonikolas;Daniel M. Kane;Vladimir Nikishkin

  • Counting arbitrary subgraphs in data streams

    Daniel M. Kane;Kurt Mehlhorn;Thomas Sauerwald;He Sun

  • The geometry of binary search trees

    Erik D. Demaine;Dion Harmon;John Iacono;Daniel Kane

  • Proceedings of the 29th Annual Conference on Learning Theory (COLT 2016)

    Ilias Diakonikolas;Daniel M. Kane;Alistair Stewart

  • Robustly Learning a Gaussian: Getting Optimal Error, Efficiently

    Alistair Stewart;Ilias Diakonikolas;Gautam Chetan Kamath;Daniel M Kane

Frequent Co-Authors

Ilias Diakonikolas
Ilias Diakonikolas University of Wisconsin–Madison
David P. Woodruff
David P. Woodruff Carnegie Mellon University
Persi Diaconis
Persi Diaconis Stanford University
Mihir Bellare
Mihir Bellare University of California, San Diego
Osamu Watanabe
Osamu Watanabe Tokyo Institute of Technology
Adam R. Klivans
Adam R. Klivans The University of Texas at Austin
Zhao Song
Zhao Song Adobe Systems (United States)

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