In this section we prove our main result. Recall that the expected matching size of π (with respect to μ) is Eμ[|^M(Π)∩E(G)|]. We shall prove the following theorem.

In this section we prove a lower bound on the sample complexity of any (ε,0)-differentially private algorithm for learning a large-margin halfspace.

We consider the problem of a single seller repeatedly selling a single item to a single buyer (specifically, the buyer has a value drawn fresh from known distribution $D$ in every round). Prior work assumes that the buyer is fully rational and will …

We address the problem of learning in an online, bandit setting where the learner must repeatedly select among $K$ actions, but only receives partial feedback based on its choices. We establish two new facts: First, using a new algorithm called Exp4…

A simple generative model has been introduced to explain the classical PMI based word embedding models, as well as recent variants involving energy-based models and matrix factorization. The model yields an optimization objective with essentially “n…

We consider the problem of approximating partition functions for Ising models. We make use of recent tools in combinatorial optimization: the Sherali-Adams and Lasserre convex programming hierarchies, in combination with variational methods to get a…

There is general consensus that learning representations is useful for a variety of reasons, e.g. efficient use of labeled data (semi-supervised learning), transfer learning and understanding hidden structure of data. Popular techniques for represen…