Learning Algorithms, Models an sPArse representations for structured DAta
The Lampada ANR is a four year project launched in November 2009 and is funded by the French National Research Agency ANR.
Lampada is a fundamental research project on machine learning and structured data. It focuses on scaling learning algorithms to handle large sets of complex data. The main challenges are 1) high dimension learning problems, 2) large sets of data and 3) dynamics of data. Complex data we consider are evolving and composed of parts in some relations. Representations of these data embed both structure and content information and are typically large sequences, trees and graphs. The main application domains are web2, social networks and biological data. The project proposes to study formal representations of such data together with incremental or sequential machine learning methods and similarity learning methods. The representation research topic includes condensed data representation, sampling, prototype selection and representation of streams of data. Machine learning methods include edit distance learning, reinforcement learning and incremental methods, density estimation of structured data and learning on streams.
Laboratoire Hubert Curien (LaHC) CNRS UMR 5516. Contact: Marc Sebban (Marc dot Sebban at univ-st-etienne dot fr).
Laboratoire d’Informatique Fondamentale de Marseille (LIF) CNRS 6166. Contact: François Denis (francois dot denis at lif dot univ-mrs dot fr)
Laboratoire d’Informatique de Paris 6 (LIP6) CNRS 7606. Contact: Patrick Galinari (Patrick dot Gallinari at lip6 dot fr)
MOSTRARE and Sequel INRIA project. Contact: Marc Tommasi (marc dot tommasi at univ-lille3 dot fr)