Probabilistic programming has emerged as a powerful paradigm that integrates uncertainty directly into computational models. By embedding probabilistic constructs into conventional programming ...

We tend to think of AI as a monolithic entity, but it has actually developed along multiple branches. One of the main branches involves performing traditional calculations but feeding the results into ...

https://doi.org/10.2307/2582400 • https://www.jstor.org/stable/2582400 Copy URL This paper offers a new approach to the solution of zero-one goal-programming ...

“Imagine a scenario in which self-driving cars fail to recognize people of color as people—and are thus more likely to hit them—because the computers were trained on data sets of photos in which such ...

In this paper, we consider two different formulations (one is smooth and the other one is nonsmooth) for solving linear matrix inequalities (LMIs), an important class of semidefinite programming (SDP) ...