Online Learning and Stochastic Approximations

While NFV is the technology that allows the virtual representation and deployment of logical network functions as Virtual Network Functions (VNFs). When these

As a novelty, the sign gradient descent algorithm allows to converge in practice towards other minima than the closest minimum of the initial condition making these algorithms

For general games, there might be non-convergence, but when the convergence of the ODE holds, considered stochastic algorithms converge towards Nash equilibria.. For games admitting

Fortunately, this is possible to go further, observing that many of the previous classes (ordinal, (exact) potential, continuous potential, load balancing games, congestion games,

This section compares Linear SVMs trained on the medium-scale and large-scale datasets using the batch Annealed NonConvex NP-SVM solver (BNP-SVM, algo- rithm 3), the online

In gossip learning, models perform random walks on the network and are trained on the local data using stochastic gradient descent.. Besides, several models can perform random walks

In this paper, we have shown that for least-squares regression, in hard problems where single-pass SGD is not statistically optimal (r < α−1 2α ), then multiple passes lead

Finally, we apply our analysis beyond the supervised learning setting to obtain convergence rates for the averaging process (a.k.a. gossip algorithm) on a graph depending on