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Microsoft Research New York City (United States)

82 papers here4.7k citations across 82
fields
  • cs.LG25
  • cs.GT14
  • cs.CY6
  • cs.SI6
  • stat.ML6
  • cs.HC5
  • cs.AI4
  • cs.DS4
ROR 056zprp28OpenAlex

affiliations via OpenAlex

output
20092026
most citedImproving fairness in machine learning systems: What do industry practitioners need?

758 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

  • Alekh Agarwal14 · h 60
  • John Langford14 · h 73
  • Aleksandrs Slivkins9 · h 38
  • Jennifer Wortman Vaughan2 profiles9 · h 43
  • A. Krishnamurthy8 · h 47
  • Miroslav Dudík8 · h 35
  • Amit Sharma5 · h 26
  • Ashton Anderson5 · h 26
  • R. Schapire5 · h 85
  • Solon Barocas5 · h 39
  • Vasilis Syrgkanis5 · h 39
  • Hal Daumé4 · h 65
collaborating institutions
  • Microsoft (United States)US53 papers
  • Cornell UniversityUS15 papers
  • Carnegie Mellon UniversityUS9 papers
  • University of Massachusetts AmherstUS8 papers
  • Princeton UniversityUS7 papers
  • Columbia UniversityUS6 papers
  • Microsoft Research (United Kingdom)GB6 papers
  • University of PennsylvaniaUS6 papers
  • University of Maryland, College ParkUS5 papers
  • University of MichiganUS5 papers
  • Harvard UniversityUS4 papers
  • Stanford UniversityUS4 papers
Showing 2011 · cs.LGShow all

1 paper · 2 filters

cs.LG2011★ 266 cited

A Reliable Effective Terascale Linear Learning System

Alekh Agarwal, Olivier Chapelle, Miroslav Dudik +1

We present a system and a set of techniques for learning linear predictors with convex losses on terascale datasets, with trillions of features, {The number of features here refers…

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