We study simple binary hypothesis testing under local differential privacy (LDP) and
communication constraints. Our results are either minimax optimal or instance optimal: the …

Information bottleneck (IB) and privacy funnel (PF) are two closely related optimization
problems which have found applications in machine learning, design of privacy algorithms …

The information bottleneck (IB) problem tackles the issue of obtaining relevant compressed
representations T of some random variable X for the task of predicting Y. It is defined as a …

The problem of splitting attributes is one of the main steps in the construction of decision
trees. In order to decide the best split, impurity measures such as Entropy and Gini are …

We consider the problem of distributed feature quantization, where the goal is to enable a
pretrained classifier at a central node to carry out its classification on features that are …

We study hypothesis testing under communication constraints, where each sample is
quantized before being revealed to a statistician. Without communication constraints, it is …

Consider the problem of binary hypothesis testing. Given Z coming from either P⊗ m or Q⊗
m, to decide between the two with small probability of error it is sufficient, and in many cases …

The main focus will be on the indirect Joint Source-Channel Coding problem in which a
noisy observation of the source has to be quantized ahead of transmission over an error …

In this article, under a general cost function C, we present a dynamic programming (DP)
method to obtain an optimal sequential deterministic quantizer (SDQ) for q-ary input discrete …

It is well known that the entropy H (X) of a discrete random variable X is always greater than
or equal to the entropy H (f (X)) of a function f of X, with equality if and only if f is one-to-one …