My research interests are on the interplay between machine learning, applied statistics and convex optimization techniques. I am interested in developing methods for pattern discovery from extremely large-scale data sets, taking uncertainty into account and respecting computational constraints. More precisely, my research focuses on the integration of multiple, heterogeneous data types for a variety of pattern discovery tasks where the amount of data is extremely large and the solutions desired to be sparse. An important challenge in the field of machine learning is to deal with the increasing amount of data that is available for learning and to leverage the (also increasing) diversity of information sources, describing these data. Beyond classical vectorial data formats, data in the format of graphs, trees, strings and beyond have become widely available for data mining, e.g., the linked structure of communication networks, amino acid sequences describing proteins, etc. Moreover, for interpretability, stability and economical reasons, decision rules that rely on a small subset of the information sources and/or a small subset of the features describing the data are highly desired: sparse learning algorithms are a must. My research is inspired by practical applications in computational genomics, financial engineering and computer music.
publication:
Lanckriet, G.R.G., Cristianini, N., Bartlett, P., El Ghaoui, L., Jordan, M.I. (2004).Learning the Kernel Matrix with Semidefinite Programming . Journal of Machine Learning Research, 5, 27-72, 2004.
2.Francis Bach-----homepage
I am a researcher at INRIA, working in the Willow project, which is located at Ecole Normale Superieure. I completed my Ph.D. in Computer Science at U.C. Berkeley, working with Professor Michael Jordan, and spent two years in the Mathematical Morphology group at Ecole des Mines de Paris. I am interested in statistical machine learning, and especially in graphical models, sparse methods, kernel-based learning, vision and signal processing. [CV (English)] [CV (French)]
publication:
F. Bach, G. R. G. Lanckriet, M. I. Jordan. Multiple Kernel Learning, Conic Duality, and the SMO Algorithm. Proceedings of the Twenty-first International Conference on Machine Learning, 2004 [pdf] [tech-report]
3.Alain Rakotomamonjy----Homepage
Research Interests
Kernel Methods and Support Vector Machines Algorithm
Regularization paths Multiple Kernel
Kernel Design Sparsity and variable selection Wavelet and Time-Frequency Signal AnalysisSignal Classification Brain-Computer Interfaces Object Recognition
Kernel Methods and Support Vector Machines Algorithm
Regularization paths Multiple Kernel
Kernel Design Sparsity and variable selection Wavelet and Time-Frequency Signal AnalysisSignal Classification Brain-Computer Interfaces Object Recognition
publication:
A. Rakotomamonjy, F. Bach, Y. Grandvalet, S. Canu, SimpleMKL, Journal of Machine Learning Research, Vol. 9, pp 2491-2521, 2008. [JMLR page][PDF] [code]
4.M. Gönen----Homepage
Research Interests
Support Vector MachinesKernel MethodsSimulation and Real-time Control of Flexible Manufacturing Systems
publication:
M. Gönen and E. Alpaydın (2008) ”Localized Multiple Kernel Learning”, In Proceedings of the 25th International Conference on Machine Learning, 352-359.
5.S. Sonnenburg----Homepage
I am currently a postdoc at the Machine Learning in Biology Group at the Friedrich Miescher Laboratory of the Max Planck Society in Tübingen. I have been working in the IDA group at the Fraunhofer Institute FIRST.
I am intrigued by sequence based machine learning methods involving large data sets and have developed several machine learning methods for bioinformatics applications such as splice site recognition, promoter detection and gene finding. I also worked on microarray analysis and motif discovery.
publication:
S. Sonnenburg, G. R¨atsch, C. Sch¨afer, and B. Sch¨olkopf. Large scale multiplekernel learning. Journal of Machine Learning Research, 7, 2006.
