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Steve Hanneke

5 papers hereh-index 12427 citations36 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author3

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.LG5
same name
  • Steve Hanneke — 5 papers
  • Steve Hanneke — 4 papers
  • Steve Hanneke — 3 papers, h 8
  • Steve Hanneke — 3 papers
  • Steve Hanneke — 3 papers
  • Steve Hanneke — 2 papers, h 9

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202022
most citedProper Learning, Helly Number, and an Optimal SVM Bound

7 citations · 9 across the 3 of their papers we have counts for

collaborators

4 papers

cs.LG2021

A Theory of PAC Learnability of Partial Concept Classes

Noga Alon, Steve Hanneke, Ron Holzman +1

We extend the theory of PAC learning in a way which allows to model a rich variety of learning tasks where the data satisfy special properties that ease the learning process. For e…

cs.LG2021★ 2 cited

Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games

Steve Hanneke, Roi Livni, Shay Moran

Which classes can be learned properly in the online model? -- that is, by an algorithm that at each round uses a predictor from the concept class. While there are simple and natura…

cs.LG2020

A Theory of Universal Learning

Olivier Bousquet, Steve Hanneke, Shay Moran +2

How quickly can a given class of concepts be learned from examples? It is common to measure the performance of a supervised machine learning algorithm by plotting its "learning cur…

cs.LG2020★ 7 cited

Proper Learning, Helly Number, and an Optimal SVM Bound

Olivier Bousquet, Steve Hanneke, Shay Moran +1

The classical PAC sample complexity bounds are stated for any Empirical Risk Minimizer (ERM) and contain an extra logarithmic factor log(1/ε) which is known to be necessary for…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.