Selective regression under fairness criteria
Selective regression allows abstention from prediction if the confidence to make an accurate
prediction is not sufficient. In general, by allowing a reject option, one expects the …
prediction is not sufficient. In general, by allowing a reject option, one expects the …
Fair learning with Wasserstein barycenters for non-decomposable performance measures
S Gaucher, N Schreuder… - … Conference on Artificial …, 2023 - proceedings.mlr.press
This work provides several fundamental characterizations of the optimal classification
function under the demographic parity constraint. In the awareness framework, akin to the …
function under the demographic parity constraint. In the awareness framework, akin to the …
Un-fair trojan: Targeted backdoor attacks against model fairness
Machine learning models have proven to have the ability to make accurate predictions on
complex data tasks such as image and graph data. However, they are vulnerable to various …
complex data tasks such as image and graph data. However, they are vulnerable to various …
Regression under demographic parity constraints via unlabeled post-processing
We address the problem of performing regression while ensuring demographic parity, even
without access to sensitive attributes during inference. We present a general-purpose post …
without access to sensitive attributes during inference. We present a general-purpose post …
A study of some trade-offs in statistical learning: online learning, generative models and fairness
N Schreuder - 2021 - theses.hal.science
Machine learning algorithms are celebrated for their impressive performance on many
tasksthat we thought were dedicated to human minds, from handwritten digits recognition …
tasksthat we thought were dedicated to human minds, from handwritten digits recognition …