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Found 11 papers, 2 papers with code
Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
In this work, we show that this problem also affects robustness to adversarial examples, thereby hindering the development of secure model update practices.
Fairness in Machine Learning
Machine learning based systems are reaching society at large and in many aspects of everyday life.
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning
One way to reach this goal is by modifying the data representation in order to meet certain fairness constraints.
Fair regression via plug-in estimator and recalibration with statistical guarantees
We study the problem of learning an optimal regression function subject to a fairness constraint.
Learning Fair and Transferable Representations
Developing learning methods which do not discriminate subgroups in the population is a central goal of algorithmic fairness.
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
We study the problem of fair binary classification using the notion of Equal Opportunity.
General Fair Empirical Risk Minimization
We tackle the problem of algorithmic fairness, where the goal is to avoid the unfairly influence of sensitive information, in the general context of regression with possible continuous sensitive attributes.
Taking Advantage of Multitask Learning for Fair Classification
In this paper we show how it is possible to get the best of both worlds: optimize model accuracy and fairness without explicitly using the sensitive feature in the functional form of the model, thereby treating different individuals equally.
Empirical Risk Minimization under Fairness Constraints
It encourages the conditional risk of the learned classifier to be approximately constant with respect to the sensitive variable.
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers
We derive here new generalization bounds, based on Rademacher Complexity theory, for model selection and error estimation of linear (kernel) classifiers, which exploit the availability of unlabeled samples.
