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Computer Science

D-Index
74
Citations
24020
World Ranking
1481
National Ranking
772

Overview

Manfred K. Warmuth is affiliated with Google in the United States and has contributed extensively to the field of Computer Science. Their research primarily focuses on Artificial Intelligence, with additional work in Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Computational Mechanics, and Computational Theory and Mathematics.

The scientist's main research topics include:

  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Algorithms
  • Neural Networks and Applications
  • Sparse and Compressive Sensing Techniques
  • Stochastic Gradient Optimization Techniques
  • Advanced Neural Network Applications
  • Machine Learning and ELM

Warmuth has published at a range of venues, with a considerable number of publications appearing in arXiv (Cornell University). Other venues include the Proceedings of the AAAI Conference on Artificial Intelligence and the Journal of Computer and System Sciences.

Selected recent papers include:

  • "Unlabeled sample compression schemes and corner peelings for ample and maximum classes," 2022, Journal of Computer and System Sciences
  • "Learning from Randomly Initialized Neural Network Features," 2022, arXiv (Cornell University)
  • "An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Step-size Adaptation Using Exponentiated Gradient Updates," 2022, arXiv (Cornell University)
  • "LocoProp: Enhancing BackProp via Local Loss Optimization," 2021, arXiv (Cornell University)

Frequent collaborators working alongside Warmuth include:

  • Ehsan Amid
  • Richard Nock
  • Rohan Anil
  • Frank Nielsen
  • Wojciech Kotłowski

Best Publications

  • The Weighted Majority Algorithm

    N. Littlestone;M. Warmuth

  • Learnability and the Vapnik-Chervonenkis dimension

    Anselm Blumer;A. Ehrenfeucht;David Haussler;Manfred K. Warmuth

  • Occam's razor

    Alselm Blumer;Andrzej Ehrenfeucht;David Haussler;Manfred K. Warmuth

  • EXPONENTIATED GRADIENT VERSUS GRADIENT DESCENT FOR LINEAR PREDICTORS

    Jyrki Kivinen;Manfred K. Warmuth

  • How to use expert advice

    Nicolò Cesa-Bianchi;Yoav Freund;David Haussler;David P. Helmbold

  • Tracking the Best Expert

    Mark Herbster;Manfred K. Warmuth

  • ON-LINE PORTFOLIO SELECTION USING MULTIPLICATIVE UPDATES

    David P. Helmbold;Robert E. Schapire;Yoram Singer;Manfred K. Warmuth

  • Active Learning with support Vector machines in the drug discovery process

    Manfred K. Warmuth;Jun Liao;Gunnar Rätsch;Michael Mathieson

  • Relative loss bounds for on-line density estimation with the exponential family of distributions

    Katy S. Azoury;M. K. Warmuth

  • Using and combining predictors that specialize

    Yoav Freund;Robert E. Schapire;Yoram Singer;Manfred K. Warmuth

  • Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension

    Sally Floyd;Manfred Warmuth

  • Computing on an anonymous ring

    Hagit Attiya;Marc Snir;Manfred K. Warmuth

  • Predicting {0, 1}-functions on randomly drawn points

    D. Haussler;N. Littlestone;M. K. Warmuth

  • Relating Data Compression and Learnability

    Nick Littlestone;Manfred K. Warmuth

  • Finding a shortest solution for the N x N extension of the 15- Puzzle is intractable

    Daniel Ratner;Manfred Warmuth

  • Equivalence of models for polynomial learnability

    David Haussler;Michael Kearns

  • Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension

    A Blumer;A Ehrenfeucht;D Haussler;M Warmuth

  • The minimum consistent DFA problem cannot be approximated within any polynomial

    Leonard Pitt;Manfred K. Warmuth

  • The (n 2 -1)-puzzle and related relocation problems

    Daniel Ratner;Manfred Warmuth

  • Randomized Online PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension

    Manfred K. Warmuth;Dima Kuzmin

  • Additive versus exponentiated gradient updates for linear prediction

    Jyrki Kivinen;Manfred K. Warmuth

  • How to use expert advice

    Nicolò Cesa-Bianchi;Yoav Freund;David P. Helmbold;David Haussler

Frequent Co-Authors

David P. Helmbold
David P. Helmbold University of California, Santa Cruz
David Haussler
David Haussler University of California, Santa Cruz
Gunnar Rätsch
Gunnar Rätsch ETH Zurich
Robert E. Schapire
Robert E. Schapire Microsoft (United States)
Peter Auer
Peter Auer University of Leoben
Nicolò Cesa-Bianchi
Nicolò Cesa-Bianchi University of Milan
Philip M. Long
Philip M. Long Google (United States)
Yoram Singer
Yoram Singer Princeton University
Daniel Hsu
Daniel Hsu Columbia University
S. V. N. Vishwanathan
S. V. N. Vishwanathan Purdue University West Lafayette

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