World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
73059
World Ranking
5449
National Ranking
2482

Research.com Recognitions

  • 2008 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to machine learning including the development of practical boosting algorithms.
  • 2004 - ACM Paris Kanellakis Theory and Practice Award Theory and Practice of Boosting

Overview

Yoav Freund is affiliated with the University of California, San Diego in the United States. Their research primarily falls within the field of Computer Science, with a focus on Artificial Intelligence, Computer Vision and Pattern Recognition, and Statistics and Probability. Their interdisciplinary interests also extend to Biophysics and Environmental Engineering.

Their work spans several key topics including:

  • Cell Image Analysis Techniques
  • AI in cancer detection
  • Digital Imaging for Blood Diseases
  • Generative Adversarial Networks and Image Synthesis
  • Medical Image Segmentation Techniques
  • Machine Learning in Healthcare
  • Explainable Artificial Intelligence (XAI)

Among recent papers, Yoav Freund has contributed to a variety of scientific publications, including:

  • CellBoost: A pipeline for machine assisted annotation in neuroanatomy, 2024, AI Open
  • StreaMRAK a streaming multi-resolution adaptive kernel algorithm, 2022, Applied Mathematics and Computation
  • When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint, 2021, arXiv (Cornell University)
  • LO76: Effect of task interruptions training on the quality of simulated ALS, 2020, Canadian Journal of Emergency Medicine
  • Experimental Design for Bathymetry Editing, 2020, arXiv (Cornell University)

Frequent coauthors who have collaborated with Yoav Freund include:

  • Kui Qian
  • David Kleinfeld
  • Beth Friedman
  • Andreas Oslandsbotn
  • Alexander Cloninger

Their publication record shows a pattern of contributions to prominent venues such as arXiv (Cornell University), AI Open, Applied Mathematics and Computation, Canadian Journal of Emergency Medicine, and bioRxiv (Cold Spring Harbor Laboratory).

Yoav Freund has received notable awards, including being named a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 2008 for significant contributions to machine learning and the development of practical boosting algorithms. Furthermore, they were awarded the ACM Paris Kanellakis Theory and Practice Award in 2004 related to the theory and practice of boosting.

Best Publications

  • A Decision Theoretic Generalization of On-Line Learning and an Application to Boosting

    Y. Freund;R. Schapire

  • Experiments with a new boosting algorithm

    Yoav Freund;Robert E. Schapire

  • A Short Introduction to Boosting

    Yoav Freund;Robert E. Schapire

  • Boosting the margin: a new explanation for the effectiveness of voting methods

    Robert E. Schapire;Yoav Freund;Peter Bartlett;Wee Sun Lee

  • An efficient boosting algorithm for combining preferences

    Yoav Freund;Raj Iyer;Robert E. Schapire;Yoram Singer

  • Boosting a weak learning algorithm by majority

    Yoav Freund

  • The Nonstochastic Multiarmed Bandit Problem

    Peter Auer;Nicolò Cesa-Bianchi;Yoav Freund;Robert E. Schapire

  • Large margin classification using the perceptron algorithm

    Yoav Freund;Robert E. Schapire

  • Selective Sampling Using the Query by Committee Algorithm

    Yoav Freund;H. Sebastian Seung;Eli Shamir;Naftali Tishby

  • The Alternating Decision Tree Learning Algorithm

    Yoav Freund;Llew Mason

  • Boosting: Foundations and Algorithms

    Robert E. Schapire;Yoav Freund

  • Gambling in a rigged casino: The adversarial multi-armed bandit problem

    P. Auer;N. Cesa-Bianchi;Y. Freund;R. Schapire

  • How to use expert advice

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

  • Boosting the margin: A new explanation for the effectiveness of voting methods

    Robert E. Schapire;Yoav Freund;Peter Barlett;Wee Sun Lee

  • Adaptive game playing using multiplicative weights

    Yoav Freund;Robert E. Schapire

  • An Adaptive Version of the Boost by Majority Algorithm

    Yoav Freund

  • Game theory, on-line prediction and boosting

    Yoav Freund;Robert E. Schapire

  • Random projection trees and low dimensional manifolds

    Sanjoy Dasgupta;Yoav Freund

  • Unsupervised learning of distributions on binary vectors using two layer networks

    Yoav Freund;David Haussler

  • How to use expert advice

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

  • Boosting

    Unknown

Frequent Co-Authors

Robert E. Schapire
Robert E. Schapire Microsoft (United States)
Christina S. Leslie
Christina S. Leslie Memorial Sloan Kettering Cancer Center
Sanjoy Dasgupta
Sanjoy Dasgupta University of California, San Diego
Yishay Mansour
Yishay Mansour Tel Aviv University
Anshul Kundaje
Anshul Kundaje Stanford University
Nicolò Cesa-Bianchi
Nicolò Cesa-Bianchi University of Milan
Peter Auer
Peter Auer University of Leoben
Kamalika Chaudhuri
Kamalika Chaudhuri University of California, San Diego
David Kleinfeld
David Kleinfeld University of California, San Diego
Daniel Hsu
Daniel Hsu Columbia University

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