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researcher

Gil Kur

15 papers hereh-index 10271 citations22 works total

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

author position
  • sole author1
  • first author7
  • middle author3
  • last author4

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • math.ST7
  • cs.LG3
  • math.MG3
  • cs.DS1
  • math.PR1
same name
  • Gil Kur — 6 papers, h 1
  • Gil Kur — 1 paper, h 1
  • Gil Kur — 1 paper, h 1

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
20172026
most citedOptimality of Maximum Likelihood for Log-Concave Density Estimation and Bounded Convex Regression

18 citations · 73 across the 13 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.LG2019★ 13 cited

Double descent in the condition number

Tomaso Poggio, Gil Kur, Andrzej Banburski

In solving a system of n linear equations in d variables Ax=b, the condition number of the n,d matrix A measures how much errors in the data b affect the solution x.…

math.MG2019★ 9 cited

Intrinsic and Dual Volume Deviations of Convex Bodies and Polytopes

Florian Besau, Steven Hoehner, Gil Kur

We establish estimates for the asymptotic best approximation of the Euclidean unit ball by polytopes under a notion of distance induced by the intrinsic volumes. We also introduce…

math.ST2019★ 18 cited

Optimality of Maximum Likelihood for Log-Concave Density Estimation and Bounded Convex Regression

Gil Kur, Yuval Dagan, Alexander Rakhlin

In this paper, we study two problems: (1) estimation of a d-dimensional log-concave distribution and (2) bounded multivariate convex regression with random design with an underly…

cs.LG2019★ 1 cited

Space lower bounds for linear prediction in the streaming model

Yuval Dagan, Gil Kur, Ohad Shamir

We show that fundamental learning tasks, such as finding an approximate linear separator or linear regression, require memory at least \emph{quadratic} in the dimension, in a natur…

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