Constrained Machine Learning: The Bagel Framework

by   Guillaume Perez, et al.

Machine learning models are widely used for real-world applications, such as document analysis and vision. Constrained machine learning problems are problems where learned models have to both be accurate and respect constraints. For continuous convex constraints, many works have been proposed, but learning under combinatorial constraints is still a hard problem. The goal of this paper is to broaden the modeling capacity of constrained machine learning problems by incorporating existing work from combinatorial optimization. We propose first a general framework called BaGeL (Branch, Generate and Learn) which applies Branch and Bound to constrained learning problems where a learning problem is generated and trained at each node until only valid models are obtained. Because machine learning has specific requirements, we also propose an extended table constraint to split the space of hypotheses. We validate the approach on two examples: a linear regression under configuration constraints and a non-negative matrix factorization with prior knowledge for latent semantics analysis.


page 1

page 2

page 3

page 4


Spherical Matrix Factorization

Matrix Factorization plays an important role in machine learning such as...

NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning

Imposing explicit constraints is relatively new but increasingly pressin...

On Regularization and Inference with Label Constraints

Prior knowledge and symbolic rules in machine learning are often express...

Robust Coreset Construction for Distributed Machine Learning

Motivated by the need of solving machine learning problems over distribu...

The empirical duality gap of constrained statistical learning

This paper is concerned with the study of constrained statistical learni...

NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning

Optimizing nonconvex (NCVX) problems, especially nonsmooth and constrain...

Sufficiently Accurate Model Learning for Planning

Data driven models of dynamical systems help planners and controllers to...

Please sign up or login with your details

Forgot password? Click here to reset