In spite of several claims stating that some models are more interpretable than others--eg,"
linear models are more interpretable than deep neural networks"--we still lack a principled …

The most widely studied explainable AI (XAI) approaches are unsound. This is the case with
well-known model-agnostic explanation approaches, and it is also the case with approaches …

Many resource management problems require sequential decision-making under
uncertainty, where the only uncertainty affecting the decision outcomes are exogenous …

Side channels provide additional information to skilled adversaries that reduce the effort to
determine an unknown key. If sufficient side channel information is available, identification of …

Total variation distance (TV distance) is a fundamental notion of distance between
probability distributions. In this work, we introduce and study the computational problem of …

We study the natural question of constructing pseudorandom generators (PRGs) for low-
degree polynomial threshold functions (PTFs). We give a PRG with seed-length log n/εO (d) …

We give the first deterministic fully polynomial-time approximation scheme (FPTAS) for
computing the partition function of a two-state spin system on an arbitrary graph, when the …

Given a multiset $ S $ of $ n $ positive integers and a target integer $ t $, the Subset Sum
problem asks to determine whether there exists a subset of $ S $ that sums up to $ t $. The …

Despite the progress observed with model-agnostic explainable AI (XAI), it is the case that
model-agnostic XAI can produce incorrect explanations. One alternative are the so-called …

Given n elements with nonnegative integer weights w_1,...,w_n and an integer capacity C,
we consider the counting version of the classic knapsack problem: find the number of distinct …