PAC-Bayesian Bounds for Sparse Regression Estimation with Exponential Weights

In the iterative procedure this problem is addressed by considering a sequence of sampling probabilities {p j } where at each step j p j (t i ) is chosen to be as big as the

Zakai, “Some lower bounds on signal parameter estimation,” IEEE Trans. Weiss, “Lower bounds on the mean square estimation error,” Proceedings of the IEEE

The aim of this paper is to provide the first accel- eration of exponential weights procedures achieving the optimal rate of convergence d 0 log(d)/(αT ) in the identically

Then we propose two improvements of this lower bound by using two different approaches; the first approach consists in adding well-chosen polynomial term to it, whereas the

We aim to obtain very tight exponential bounds for hyperbolic tangent by first establishing simple algebraic bounds for this function.. Our inequalities refine a double inequality

We consider an evolution equation similar to that introduced by Vese in [12] and whose solution converges in large time to the convex envelope of the initial datum.. Starting from

Now, another example is the wind speed (Jacq et al., 2005) associated with the mistral gust: one may construct a D-valued ARMA model and note that, under mild conditions, the model

Theorem 2.1 provides an asymptotic result for the ridge estimator while Theorem 2.2 gives a non asymptotic risk bound of the empirical risk minimizer, which is complementary to