Guillaume Stempfel

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3ranked-venue papers

2as first author

0since 2021 · last 2010

—ORCID · none

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

Artificial intelligence and machine learning · 3 · 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

1 paper

Learning theory · 75% Efficient and distributed learning · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Efficient and distributed learning › federated learning data heterogeneity	0.1	1	2010	Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes · J. Mach. Learn. Res. 2010
Machine learning › Learning theory generalization bounds	0.1	1	2010	Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes · J. Mach. Learn. Res. 2010
Machine learning › Learning theory › statistical learning theory › non-i.i.d. learning learning with dependent data	0.1	1	2010	Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes · J. Mach. Learn. Res. 2010
Machine learning › Learning theory › generalization bounds PAC-Bayes bounds	0.1	1	2010	Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes · J. Mach. Learn. Res. 2010

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

stationary β-mixing process · 0.1chromatic PAC-Bayes · 0.1

Year	Publication	Venue	Position
2010	Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes Liva Ralaivola, Marie Szafranski, Guillaume Stempfel	J. Mach. Learn. Res.	3
2009	Learning SVMs from Sloppily Labeled Data Guillaume Stempfel, Liva Ralaivola	ICANN (1)	1
2007	Learning Kernel Perceptrons on Noisy Data Using Random Projections Guillaume Stempfel, Liva Ralaivola	ALT	1