M
Maurizio Filippone
Researcher at Institut Eurécom
Publications - 147
Citations - 3496
Maurizio Filippone is an academic researcher from Institut Eurécom. The author has contributed to research in topics: Gaussian process & Inference. The author has an hindex of 26, co-authored 133 publications receiving 2908 citations. Previous affiliations of Maurizio Filippone include University of Glasgow & University UCINF.
Papers
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Journal ArticleDOI
A survey of kernel and spectral methods for clustering
TL;DR: A survey of kernel and spectral clustering methods, two approaches able to produce nonlinear separating hypersurfaces between clusters and an explicit proof of the fact that these two paradigms have the same objective is reported.
Journal ArticleDOI
A comparative evaluation of outlier detection algorithms: Experiments and analyses
TL;DR: This task challenges state-of-the-art methods from a variety of research fields to applications including fraud detection, intrusion detection, medical diagnoses and data cleaning.
Journal ArticleDOI
Aggregation Algorithm Towards Large-Scale Boolean Network Analysis
TL;DR: It is shown that finding the best acyclic aggregation is equivalent to finding the strongly connected components of the network graph, and the efficiency of the proposed algorithm is demonstrated on two biological systems, namely a T-cell receptor network and an early flower development network.
Proceedings Article
MCMC for variationally sparse Gaussian processes
TL;DR: A Hybrid Monte-Carlo sampling scheme which allows for a non-Gaussian approximation over the function values and covariance parameters simultaneously, with efficient computations based on inducing-point sparse GPs.
Proceedings Article
ODE parameter inference using adaptive gradient matching with Gaussian processes
TL;DR: The present paper discusses a method based on nonparametric Bayesian statistics with Gaussian processes due to Calderhead et al. (2008), and shows how inference in this model can be substantially improved by consistently inferring all parameters from the joint dis- tribution.