Search Results for author:
Found 10 papers, 4 papers with code
On the Impact of Output Perturbation on Fairness in Binary Linear Classification
We theoretically study how differential privacy interacts with both individual and group fairness in binary linear classification.
A Revenue Function for Comparison-Based Hierarchical Clustering
Comparison-based learning addresses the problem of learning when, instead of explicit features or pairwise similarities, one only has access to comparisons of the form: \emph{Object $A$ is more similar to $B$ than to $C$.}
Differential Privacy has Bounded Impact on Fairness in Classification
We theoretically study the impact of differential privacy on fairness in classification.
FairGrad: Fairness Aware Gradient Descent
We address the problem of group fairness in classification, where the objective is to learn models that do not unjustly discriminate against subgroups of the population.
Near-Optimal Comparison Based Clustering
The goal of clustering is to group similar objects into meaningful partitions.
Foundations of Comparison-Based Hierarchical Clustering
We address the classical problem of hierarchical clustering, but in a framework where one does not have access to a representation of the objects or their pairwise similarities.
Boosting for Comparison-Based Learning
We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form "object $x_i$ is closer to object $x_j$ than to object $x_k$."
Mapping Estimation for Discrete Optimal Transport
Most of the computational approaches of Optimal Transport use the Kantorovich relaxation of the problem to learn a probabilistic coupling $\mgamma$ but do not address the problem of learning the underlying transport map $\funcT$ linked to the original Monge problem.
Regressive Virtual Metric Learning
In this paper, instead of bringing closer examples of the same class and pushing far away examples of different classes we propose to move the examples with respect to virtual points.
