Learning Algorithms, Models an sPArse representations for structured DAta
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.
Open positions
- Master Internship : Node classification of a graph via sampling
- Master Internship : Comparing Algorithms for Graph Classification
- Post doctoral position in Lille (Mostrare, Inria) (published: feb 2010)
- Post doctoral position in Marseille (LIF) (published: january 2010)
Welcome!
Nicolas Pinchaud has joined the group. Nicolas works on discovering features, transfer learning, complex actions in reinforcement learning.