Maties Machine Learning (MML) is a seminar series and discussion forum with the goal of bringing together people working on machine learning at Stellenbosch University. We meet roughly every second week for short talks on people’s current work, some ML-related topic, or open discussions. The idea is to get to know what others are working on and to strengthen machine learning research at Stellenbosch.
1 April 2022, 13:00-13:50 in the General Engineering Building, room A403A
Mike Gartrell - An introduction to scalable nonsymmetric determinantal point processes
Determinantal point processes (DPPs) have attracted substantial attention as an elegant probabilistic model that captures the balance between quality and diversity within sets. A DPP assigns a probability to every subset of a collection of items, and are conventionally parameterized by a positive semi-definite kernel matrix; this symmetric kernel encodes only repulsive interactions between items. These so-called symmetric DPPs have been successfully applied to a variety of machine learning tasks, including recommendation systems, information retrieval, and automatic summarization, among many others. Efficient algorithms for learning symmetric DPPs and sampling from these models have been reasonably well studied. However, until recently, relatively little attention has been given to nonsymmetric DPPs, which relax the symmetric constraint on the kernel. Nonsymmetric DPPs allow for both repulsive and attractive item interactions, which can significantly improve modeling power, resulting in a model that may be a better fit for some applications. In this talk we introduce both symmetric and nonsymmetric DPPs, and present our recent work on scalable methods for nonsymmetric DPPs, including learning, maximum a posteriori inference (finding the most probable subset), and sampling.