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Alexandre Gramfort

Inria

91 papers hereh-index 61112.9k citations273 works total

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

author position
  • first author1
  • middle author48
  • last author36

Across the 85 of 91 papers where every author was matched, so the position is known.

fields
  • stat.ML44
  • cs.LG18
  • eess.SP9
  • cs.CV4
  • q-bio.NC4
  • stat.AP4
affiliations
  • Inria
Homepage
same name
  • Alexandre Gramfort — 7 papers, h 4
  • Alexandre Gramfort — 4 papers
  • Alexandre Gramfort — 3 papers, h 2
  • Alexandre Gramfort — 3 papers, h 4

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
20102026
most citedScikit-learn: Machine Learning in Python

63.6k citations · 65.9k across the 85 of their papers we have counts for

collaborators
Showing 2016 · stat.MLShow all

4 papers · 2 filters

stat.ML2016★ 11 cited

Gap Safe screening rules for sparsity enforcing penalties

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +1

In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term. A recently introduced technique called screening rules…

stat.ML2016

Anomaly Detection and Localisation using Mixed Graphical Models

Romain Laby, François Roueff, Alexandre Gramfort

We propose a method that performs anomaly detection and localisation within heterogeneous data using a pairwise undirected mixed graphical model. The data are a mixture of categori…

stat.ML2016★ 22 cited

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +2

In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance. It is customary to consider ℓ1​ penalty to enf…

stat.ML2016★ 32 cited

GAP Safe Screening Rules for Sparse-Group-Lasso

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +1

In high dimensional settings, sparse structures are crucial for efficiency, either in term of memory, computation or performance. In some contexts, it is natural to handle more ref…

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