Filter by type:
Near-Optimal Comparison Based Clustering
The goal of clustering is to group similar objects into meaningful partitions. This process is well understood when an explicit …
Too Relaxed to be Fair
We address the problem of classification under fairness constraints. Given a notion of fairness, the goal is to learn a classifier that …
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 …
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 …
Mapping Estimation for Discrete Optimal Transport
We are interested in the computation of the transport map of an Optimal Transport problem. Most of the computational approaches of …
Regressive Virtual Metric Learning
We are interested in supervised metric learning of Mahalanobis like distances. Existing approaches mainly focus on learning a new …
A Theoretical Analysis of Metric Hypothesis Transfer Learning
We consider the problem of transferring some a priori knowledge in the context of supervised metric learning approaches. While this …
Speeding Up Syntactic Learning using Contextual Information
It has been shown in (Angluin and Becerra-Bonache, 2010, 2011) that interactions between a learner and a teacher can help language …