Gilles Meyer

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2ranked-venue papers

2as first author

0since 2021 · last 2011

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 2 papers		Optimization for machine learning · 67% Learning theory · 33%
Theoretical computer science 1 paper		Mathematical optimization · 50% Algorithms and data structures · 50%

Topics — the 5 heaviest of 5, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Optimization for machine learning constrained optimization	0.1	1	2011	Linear Regression under Fixed-Rank Constraints: A Riemannian Approach · ICML 2011
Machine learning › Learning theory › high-dimensional statistics matrix estimation	0.1	1	2011	Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach · J. Mach. Learn. Res. 2011
Machine learning › Optimization for machine learning riemannian optimization	0.1	1	2011	Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach · J. Mach. Learn. Res. 2011
Algorithms and data structures › numerical linear algebra linear regression	0.1	1	2011	Linear Regression under Fixed-Rank Constraints: A Riemannian Approach · ICML 2011
Mathematical optimization riemannian optimization	0.1	1	2011	Linear Regression under Fixed-Rank Constraints: A Riemannian Approach · ICML 2011

Methods — techniques the papers use, named apart from their topics

riemannian approach · 0.2riemannian gradient descent · 0.1

Year	Publication	Venue	Position
2011	Linear Regression under Fixed-Rank Constraints: A Riemannian Approach Gilles Meyer, Silvère Bonnabel, Rodolphe Sepulchre	ICML	1
2011	Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach Gilles Meyer, Silvère Bonnabel, Rodolphe Sepulchre	J. Mach. Learn. Res.	1