A general-purpose toolbox for efficient Kronecker-based learning
2019-07-23, 11:30–11:40, Elm A

Pairwise learning is a machine learning paradigm where the goal is to predict properties of pairs of objects. Applications include recommender systems, such as used by Amazon, molecular network inference and ecological interaction prediction. Kronecker-based learning systems provide a simple, yet elegant method to learn from such pairs. Using tricks from linear algebra, these models can be trained, tuned and validated on large datasets. The Julia package Kronecker.jl aggregates these tricks, such that it is easy to build such learning systems.

I would like to introduce the Kronecker kernel-based framework I developed during my PhD and explain why I would switch from Python to Julia for this.