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The Algorithm Configuration Problem
The field of algorithmic optimization has significantly advanced with the development of methods for the automatic configuration of algorithmic parameters.
Learning to Configure Mathematical Programming Solvers by Mathematical Programming
We discuss the issue of finding a good mathematical programming solver configuration for a particular instance of a given problem, and we propose a two-phase approach to solve it.
A learning-based mathematical programming formulation for the automatic configuration of optimization solvers
We propose a methodology, based on machine learning and optimization, for selecting a solver configuration for a given instance.
Structured Pruning of Neural Networks for Constraints Learning
In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity across diverse applications, including cancer treatment, algorithmic configuration, and chemical process optimization.
On the Convergence of Stochastic Gradient Descent in Low-precision Number Formats
Deep learning models are dominating almost all artificial intelligence tasks such as vision, text, and speech processing.
Deep Neural Networks pruning via the Structured Perspective Regularization
In Machine Learning, Artificial Neural Networks (ANNs) are a very powerful tool, broadly used in many applications.
