Input-agnostic certified group fairness via gaussian parameter smoothing
Only recently, researchers attempt to provide classification algorithms with provable group
fairness guarantees. Most of these algorithms suffer from harassment caused by the …
fairness guarantees. Most of these algorithms suffer from harassment caused by the …
Fair price discrimination
A seller is pricing identical copies of a good to a stream of unit-demand buyers. Each buyer
has a value on the good as his private information. The seller only knows the empirical value …
has a value on the good as his private information. The seller only knows the empirical value …
Beyond adult and compas: Fairness in multi-class prediction
We consider the problem of producing fair probabilistic classifiers for multi-class
classification tasks. We formulate this problem in terms of" projecting" a pre-trained (and …
classification tasks. We formulate this problem in terms of" projecting" a pre-trained (and …
Fair decision-making for food inspections
We revisit the application of predictive models by the Chicago Department of Public Health
to schedule restaurant inspections and prioritize the detection of critical food code violations …
to schedule restaurant inspections and prioritize the detection of critical food code violations …
Generalizing group fairness in machine learning via utilities
Group fairness definitions such as Demographic Parity and Equal Opportunity make
assumptions about the underlying decision-problem that restrict them to classification …
assumptions about the underlying decision-problem that restrict them to classification …
Fairee: fair classification with finite-sample and distribution-free guarantee
Algorithmic fairness plays an increasingly critical role in machine learning research. Several
group fairness notions and algorithms have been proposed. However, the fairness …
group fairness notions and algorithms have been proposed. However, the fairness …
Enhancing fairness in classification tasks with multiple variables: a data-and model-agnostic approach
Nowadays assuring that search and recommendation systems are fair and do not apply
discrimination among any kind of population has become of paramount importance. Those …
discrimination among any kind of population has become of paramount importance. Those …
Non-invasive fairness in learning through the lens of data drift
Machine Learning models are widely employed to drive many modern data systems. While
they are undeniably powerful tools, ML models often demonstrate imbalanced performance …
they are undeniably powerful tools, ML models often demonstrate imbalanced performance …
Fairness through counterfactual utilities
Group fairness definitions such as Demographic Parity and Equal Opportunity make
assumptions about the underlying decision-problem that restrict them to classification …
assumptions about the underlying decision-problem that restrict them to classification …
Information-Theoretic Tools for Machine Learning Beyond Accuracy
H Hsu - 2023 - search.proquest.com
For the past decades, information theory and machine learning have propelled each other
forward. Information theory has provided mathematical tools to tackle emerging challenges …
forward. Information theory has provided mathematical tools to tackle emerging challenges …