Neural embedding models have become a fundamental component of modern information retrieval (IR) pipelines.

In this work, we provide a general framework for studying these problems, which we refer to as Responsible AI (RAI) games .

More precisely, we prove three different ways to upper bound the intrinsic uncertainty, as described by the conditional entropy of the target variable given the inputs, by combining CP with information theoretical inequalities.

However, due to the heterogeneity between the clients' data distributions, the model obtained through the use of FL algorithms may perform poorly on some client's data.

These techniques calibrate an initial non-metric distance matrix estimated on incomplete data to a distance metric.

In this paper, we explore two fundamental first-order algorithms in convex optimization, namely, gradient descent (GD) and proximal gradient method (ProxGD). Our focus is on making these algorithms entirely adaptive by leveraging local curvature information of smooth functions.

First, we define distributionally equivalent and Max-Information model extraction attacks, and reduce them into a variational optimisation problem.