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Online learning with exponential weights in metric spaces
This paper addresses the problem of online learning in metric spaces using exponential weights.
The exponentially weighted average forecaster in geodesic spaces of non-positive curvature
We shortly discuss the application of these results in the context of aggregation and for the problem of barycenter estimation.
A notion of stability for k-means clustering
In this paper, we define and study a new notion of stability for the $k$-means clustering scheme building upon the notion of quantization of a probability measure.
On the Exponentially Weighted Aggregate with the Laplace Prior
These inequalities show that if the temperature parameter is small, the EWA with the Laplace prior satisfies the same type of oracle inequality as the lasso estimator does, as long as the quality of estimation is measured by the prediction loss.
On the prediction loss of the lasso in the partially labeled setting
In this paper we revisit the risk bounds of the lasso estimator in the context of transductive and semi-supervised learning.
