Posterior distributions depend on the modeling assumptions and can rarely be computed exactly. V ariational Inference (VI) is a technique to approximate posterior distributions through optimization. It involves choosing a set of tractable densities, a.k.a.

Sketching and stochastic gradient methods are arguably the most common techniques to derive efficient large scale learning algorithms. In this paper, we investigate their application in the context of nonparametric statistical learning.

Time series are ubiquitous in many classification/regression applications.

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Neural Information Processing Systems http://nips.cc/

Both authors contributed equally to this work.

In our output perturbation method, the parties combine local models within a secure computation and then add the required differential privacy noise before revealing the model.

Knowing when a classifier's prediction can be trusted is useful in many applications

Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set.

In a number of disciplines, the data (e.g., graphs, manifolds) to be analyzed are