Note that the simple learning algorithm is an important contribution: the algorithm for a Gaussian prior is quite a bit more complicated, and previous related work with the Laplacian prior (two-sided exponential) has had a difficult time finding learning algorithms; because the Laplacian does not have a continuous first derivative, and because the exponential prior is bounded at 0, standard gradient descent type algorithms may exhibit poor behavior.
it turns out to be an extremely weak one -it is not uncommon for models, especially those that use all or most possible features, to assign near-zero probabilities (or, if λs may be infi-nite, even actual zero probabilities), and to exhibit other symptoms of severe overfitting.