Integer and linear programming methods for two problems in machine learning

Abstract: In this talk we describe linear and integer programming based methods for two problems in statistics and machine learning. In the first part, we focus on learning causal structures in the presence of latent variables, which can be modeled as the problem of optimizing over the set of acyclic directed mixed graphs (containing directed and bidirected edges) defined on a set of nodes. We give an integer programming formulation of this problem, and the first algorithm to solve this problem to optimality (via branch-and-cut). In the second part of the talk, we will describe a linear programming based approach to learning first-order logical rules for the knowledge graph link prediction problem. This is joint work with Rui Chen, Tian Gao, and Joao Goncalves.

game theorymachine learningmathematical softwarecomputer science theorycombinatoricsoptimization and control

Audience: researchers in the topic

Mixed Integer Programming Workshop 2021

Series comments: The 18th Mixed Integer Programming Workshop will be held online on May 24-27, 2021.

It will feature 21 distinguished invited speakers covering most aspects of Mathematical Optimization, an interactive, gamified MIP student poster session with 50 posters, and a casual business meeting.

Registration is free of charge. Register here: fico.zoom.us/webinar/register/2416186463858/WN_DVLhGOToQkKyvKYPiA4cQw

Find the website of MIP2021 at sites.google.com/view/mipworkshop2021/.