Declarative Programming for Statistical ML: The democratization of complex data does not mean dropping the data on everyone’s desk and saying, “good luck”! It means to make machine learning methods usable in such a way that people can easily instruct machines to have a “look” at complex data and help them to understand and act on it. Existing statistical relational learning and probabilistic programming languages only provide partial solutions; most of them do not support convex optimization commonly used in machine learning.
In this talk I will present RELOOP, a declarative mathematical programming language embedded into Python. It allows the user to specify mathematical programs before she knows what individuals are in the domain and, therefore, before she knows what variables and constraints exist. It facilitates the formulation of abstract, general knowledge. And, it reveals the rich logical structure underlying many machine learning problems to the solver and, turn, may make it go faster.
With this, people can start to rapidly develop statistical machine learning approaches for complex data. For instance, adding just three lines of RELOOP code makes a linear support vector machines aware of any underlying network that connects the objects to be classified.
Joint work with Martin Mladenov and many others.