Resilient Constrained Learning

by   Ignacio Hounie, et al.

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 and generalization guarantees. We showcase the advantages of this resilient learning method in image classification tasks involving multiple potential invariances and in heterogeneous federated learning.


page 1

page 2

page 3

page 4


The empirical duality gap of constrained statistical learning

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

Safe Policies for Reinforcement Learning via Primal-Dual Methods

In this paper, we study the learning of safe policies in the setting of ...

Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations

Dimensionality Reduction is a commonly used element in a machine learnin...

Scaff-PD: Communication Efficient Fair and Robust Federated Learning

We present Scaff-PD, a fast and communication-efficient algorithm for di...

Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation

We study the exploration-exploitation dilemma in the linear quadratic re...

Robot Design With Neural Networks, MILP Solvers and Active Learning

Central to the design of many robot systems and their controllers is sol...

Fast Design Space Exploration of Nonlinear Systems: Part I

System design tools are often only available as blackboxes with complex ...

Please sign up or login with your details

Forgot password? Click here to reset