Learning and Optimization of Blackbox Combinatorial Solvers in Neural Networks
This work addresses the challenge of enhancing neural network performance with efficient solvers for machine learning practitioners, but it is incremental as it builds on existing blackbox integration techniques.
The paper tackles the problem of integrating blackbox combinatorial solvers into neural networks by optimizing both the primary loss and solver performance via Time-cost Regularization, and introduces a hyper-blackbox method to learn solver parameters, achieving improved efficiency in experiments.
The use of blackbox solvers inside neural networks is a relatively new area which aims to improve neural network performance by including proven, efficient solvers for complex problems. Existing work has created methods for learning networks with these solvers as components while treating them as a blackbox. This work attempts to improve upon existing techniques by optimizing not only over the primary loss function, but also over the performance of the solver itself by using Time-cost Regularization. Additionally, we propose a method to learn blackbox parameters such as which blackbox solver to use or the heuristic function for a particular solver. We do this by introducing the idea of a hyper-blackbox which is a blackbox around one or more internal blackboxes.