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researcher

Bin Yu

59 papers hereh-index 6829.8k citations211 works total

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

author position
  • sole author2
  • first author3
  • middle author5
  • last author48

Across the 58 of 59 papers where every author was matched, so the position is known.

fields
  • stat.ML26
  • math.ST13
  • stat.ME6
  • stat.AP5
  • cs.LG4
  • q-bio.NC2
same name
  • Bin Yu — 18 papers, h 6
  • Bin Yu — 13 papers, h 6
  • Bin Yu — 10 papers, h 5
  • Bin Yu — 9 papers, h 5
  • Bin Yu — 7 papers, h 38
  • Bin Yu — 7 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
20052021
most citedMeta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning

1.1k citations · 5.4k across the 59 of their papers we have counts for

collaborators
Showing 2009 · math.STShow all

3 papers · 2 filters

math.ST2009★ 5 cited

Minimax rates of estimation for high-dimensional linear regression over ℓq​-balls

Garvesh Raskutti, Martin J. Wainwright, Bin Yu

Consider the standard linear regression model $\y = \Xmat \betastar + w$, where $\y \in \real^\numobs$ is an observation vector, $\Xmat \in \real^{\numobs \times \pdim}$ is a desig…

math.ST2009★ 653 cited

The composite absolute penalties family for grouped and hierarchical variable selection

Peng Zhao, Guilherme Rocha, Bin Yu

Extracting useful information from high-dimensional data is an important focus of today's statistical research and practice. Penalized loss function minimization has been shown to…

math.ST2009★ 20 cited

Asymptotic distribution and sparsistency for l1-penalized parametric M-estimators with applications to linear SVM and logistic regression

Guilherme V. Rocha, Xing Wang, Bin Yu

Since its early use in least squares regression problems, the l1-penalization framework for variable selection has been employed in conjunction with a wide range of loss functions…

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