Nadav Eiron

Statistical Learning Theory · 1998–2003

A researcher in the Science corpus: 4 papers; trusts 7 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

Whom Nadav Eiron trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Leslie G. Valiant
  2. Michael Kearns
  3. Nick Littlestone
  4. Avrim Blum
  5. Nicholas I. Fisher
  6. Robert E. Schapire
  7. R. Rivest
  8. Ming Li
  9. Yoav Freund

Who trusts Nadav Eiron overall

  1. Frank J. Balbach
  2. Xiaojin Zhu
  3. Saeed Mahloujifar
  4. Adam N. Elmachtoub
  5. Ulrich Rückert
  6. Shai Ben-David
  7. Nikola Konstantinov
  8. Huang Xiao
  9. Thomas Zeugmann
  10. Fei Wu

Papers from Nadav Eiron's perspective

Ordered by how strongly the researchers Nadav Eiron trusts endorse each paper. Same corpus, a different order for every researcher.

  1. A theory of the learnable
  2. Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm
  3. Learning in the presence of malicious errors
  4. Probability Inequalities for Sums of Bounded Random Variables
  5. Learning Disjunction of Conjunctions
  6. The Strength of Weak Learnability
  7. A decision-theoretic generalization of on-line learning and an application to boosting
  8. On the Density of Families of Sets
  9. On the complexity of teaching
  10. Weakly learning DNF and characterizing statistical query learning using Fourier analysis

Their papers