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64 papers here2.2k citations across 64
fields
  • cs.LG14
  • cs.CL7
  • cs.SI7
  • cs.DS6
  • cs.CV4
  • stat.ML3
  • astro-ph.IM2
  • cs.AI2
ROR 038p3gq39OpenAlex

affiliations via OpenAlex

output
20042022
most citedFeUdal Networks for Hierarchical Reinforcement Learning

252 citations

researchers with a paper here
  • M. Lalmas6 · h 53
  • Satyen Kale6 · h 46
  • D. Quercia4 · h 45
  • Eduardo Graells-Garrido4 · h 18
  • John Langford4 · h 73
  • L. Aiello4 · h 36
  • F. Bonchi3 · h 63
  • Joel R. Tetreault3 · h 44
  • Miroslav Dudík3 · h 35
  • N. Kourtellis3 · h 35
  • R. Baeza-Yates3 · h 48
  • A. Beygelzimer2 · h 27
collaborating institutions
  • Yahoo (United States)US11 papers
  • Yahoo (Spain)ES8 papers
  • Telefonica Research and DevelopmentES7 papers
  • IBM (United States)US4 papers
  • University of CambridgeGB4 papers
  • Cornell UniversityUS3 papers
  • Johns Hopkins UniversityUS3 papers
  • Microsoft (United States)US3 papers
  • University of ChicagoUS3 papers
  • Columbia UniversityUS2 papers
  • Georgia Institute of TechnologyUS2 papers
  • Google (United States)US2 papers
Showing 2011Show all

1 paper · 1 filter

cs.LG2011★ 119 cited

Efficient Optimal Learning for Contextual Bandits

Miroslav Dudik, Daniel Hsu, Satyen Kale +4

We address the problem of learning in an online setting where the learner repeatedly observes features, selects among a set of actions, and receives reward for the action taken. We…

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