Can I read Resilient Constrained Learning on EtoBox?

Resilient Constrained Learning by Hounie, Ignacio; Ribeiro, Alejandro; Chamon, Luiz F. O. is a scholarly article available to read on EtoBox.

What is Resilient Constrained Learning about?

When deploying machine learning solutions, they must satisfy multiple requirements beyond accuracy, such as fairness, robustness, or safety. These requirements are imposed during training either implicitly, using penalties, or explicitly, using constrained optimization methods based on Lagrangian duality. Either way, specifying requirements is hindered by the presence of compromises and limited prior knowledge about the data. Furthermore, their impact on performance can often only be evaluated by actually solving the learning problem. This paper presents a constrained learning approach that adapts the requirements while simultaneously solving the learning task. To do so, it relaxes the learning constraints in a way that contemplates how much they affect the task at hand by balancing the performance gains obtained from the relaxation against a user-defined cost of that relaxation. We call this approach resilient constrained learning after the term used to describe ecological systems that adapt to disruptions by modifying their operation. We show conditions under which this balance can be achieved and introduce a practical algorithm to compute it, for which we derive approximation an

Author

Hounie, Ignacio; Ribeiro, Alejandro; Chamon, Luiz F. O.

Published

2023

Language

EN