Demo Data from SIGIR 2026 / reviewers in the wild / expert
Nicolas Delannay
dblp:81/6142
· DBLP profile ↗
8ranked-venue papers
4as first author
0since 2021 · last 2008
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 7 · 3 first-authorDatabases, data management, data science and information retrieval · 1 · 1 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 |
Representation and self-supervised learning · 83% Trustworthy machine learning · 17% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Representation and self-supervised learning › multi-view learning
canonical correlation analysis |
0.1 | 1 | 2006 | Robust probabilistic projections · ICML 2006 |
Machine learning › Representation and self-supervised learning › representation learning
dimensionality reduction |
0.1 | 1 | 2006 | Robust probabilistic projections · ICML 2006 |
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction
probabilistic principal component analysis |
0.1 | 1 | 2006 | Robust probabilistic projections · ICML 2006 |
Machine learning › Trustworthy machine learning › robustness
outlier robustness |
0.0 | 1 | 2006 | Robust probabilistic projections · ICML 2006 |
Machine learning › Trustworthy machine learning
robustness |
0.0 | 1 | 2006 | Robust probabilistic projections · ICML 2006 |
Methods — techniques the papers use, named apart from their topics
student-t distribution · 0.1maximum likelihood estimation · 0.1EM algorithm · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Improving the Robustness to Outliers of Mixtures of Probabilistic PCAs
Nicolas Delannay, Cédric Archambeau, Michel Verleysen |
PAKDD | 1 |
| 2008 | Mixtures of robust probabilistic principal component analyzers
Cédric Archambeau, Nicolas Delannay, Michel Verleysen |
Neurocomputing | 2 |
| 2008 | Collaborative filtering with interlaced generalized linear models
Nicolas Delannay, Michel Verleysen |
Neurocomputing | 1 |
| 2007 | Mixtures of robust probabilistic principal component analyzers
Cédric Archambeau, Nicolas Delannay, Michel Verleysen |
ESANN | 2 |
| 2007 | Collaborative Filtering with interlaced Generalized Linear Models
Nicolas Delannay, Michel Verleysen |
ESANN | 1 |
| 2006 | Robust probabilistic projectionsabstractPrincipal components and canonical correlations are at the root of many exploratory data mining techniques and provide standard pre-processing tools in machine learning. Lately, probabilistic reformulations of these methods have been proposed (Roweis, 1998; Tipping & Bishop, 1999b; Bach & Jordan, 2005). They are based on a Gaussian density model and are therefore, like their non-probabilistic counterpart, very sensitive to atypical observations. In this paper, we introduce robust probabilistic principal component analysis and robust probabilistic canonical correlation analysis. Both are based on a Student-t density model. The resulting probabilistic reformulations are more suitable in practice as they handle outliers in a natural way. We compute maximum likelihood estimates of the parameters by means of the EM algorithm. Cédric Archambeau, Nicolas Delannay, Michel Verleysen |
ICML | 2 |
| 2005 | Representation of functional data in neural networks
Fabrice Rossi, Nicolas Delannay, Brieuc Conan-Guez, Michel Verleysen |
Neurocomputing | 2 |
| 2004 | Functional radial basis function networks
Nicolas Delannay, Fabrice Rossi, Brieuc Conan-Guez, Michel Verleysen |
ESANN | 1 |