Structured prediction tasks pose a fundamental trade-off between the need for model complexity to increase predictive power and the limited computational resources for inference i...
We describe a novel inference algorithm for sparse Bayesian PCA with a zero-norm prior on the model parameters. Bayesian inference is very challenging in probabilistic models of t...
Reliable estimation of the classification performance of learned predictive models is difficult, when working in the small sample setting. When dealing with biological data it is ...
Antti Airola, Tapio Pahikkala, Willem Waegeman, Be...
In this paper, we present multiple novel applications for local intrinsic dimension estimation. There has been much work done on estimating the global dimension of a data set, typi...
An operational framework is developed for testing stationarity relatively to an observation scale, in both stochastic and deterministic contexts. The proposed method is based on a ...
Pierre Borgnat, Patrick Flandrin, Paul Honeine, C&...