Lifted Machine Learning

Our minds make inferences that appear to go far beyond standard data science approaches. Whereas people can learn richer representations and use them for a wider range of data science tasks, data science algorithms have been mainly employed in a stand-alone context, constructing a single function from a table of training examples. In this seminar, I shall touch upon an approach to data science that can capture these human learning aspects by combining graphs, databases, and relational logic in general with statistical learning and optimization. Here, high-level (logical) features such as individuals, relations, functions, and connectives provide declarative clarity and succinct characterizations of the data science problem. While attractive from a modeling viewpoint, this declarative data science programming also often assuredly complicates the underlying model, making solving it potentially very slow. Hence, I shall also touch upon ways to reduce the solver costs. One promising direction to speed up is to cache local structures in the computational models. I shall illustrate this for probabilistic inference, linear programs, and convex quadratic programs, all working horses of data science.