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researcher

S. Shekhar

20 papers hereh-index 13487 citations35 works total

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

author position
  • first author15
  • middle author2
  • last author2

Across the 19 of 20 papers where every author was matched, so the position is known.

fields
  • cs.LG5
  • stat.ML5
  • math.ST4
  • stat.ME3
  • cs.RO1
  • q-bio.PE1
same name
  • S. Shekhar — 18 papers, h 34
  • S. Shekhar — 13 papers, h 60
  • S. Shekhar — 5 papers, h 46
  • S. Shekhar — 4 papers, h 2
  • S. Shekhar — 2 papers, h 9
  • S. Shekhar — 2 papers, h 2

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
20172025
most citedSpecies tree estimation using ASTRAL: how many genes are enough?

38 citations · 70 across the 19 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

stat.ML2019

Adaptive Sampling for Estimating Multiple Probability Distributions

Shubhanshu Shekhar, Tara Javidi, Mohammad Ghavamzadeh

We consider the problem of allocating samples to a finite set of discrete distributions in order to learn them uniformly well in terms of four common distance measures: ℓ22​,…

cs.LG2019★ 6 cited

Active Learning for Binary Classification with Abstention

Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi

We construct and analyze active learning algorithms for the problem of binary classification with abstention. We consider three abstention settings: \emph{fixed-cost} and two varia…

cs.LG2019★ 5 cited

Binary Classification with Bounded Abstention Rate

Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi

We consider the problem of binary classification with abstention in the relatively less studied \emph{bounded-rate} setting. We begin by obtaining a characterization of the Bayes o…

stat.ML2019★ 2 cited

Multiscale Gaussian Process Level Set Estimation

Shubhanshu Shekhar, Tara Javidi

In this paper, the problem of estimating the level set of a black-box function from noisy and expensive evaluation queries is considered. A new algorithm for this problem in the Ba…

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