Clustering
Coresets for Clustering with Missing Values
Previous coreset constructions only allow one missing coordinate.
Coresets for Clustering with Missing Values
Previous coreset constructions only allow one missing coordinate.
From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering Ines Chami Albert Gu Vaggos Chatziafratis Christopher Rรฉ
In this work, we provide the first continuous relaxation of Dasgupta's discrete optimization problem with provable quality guarantees.
Efficient Clustering Based On A Unified View Of K-means And Ratio-cut
Much effort has been devoted to accelerate the process.
KFC: A Scalable Approximation Algorithm for k-center Fair Clustering
However, there is an additional constraint that each cluster needs to be fair, under some notion of fairness. This ensures that no group is either "over-represented" or "under-represented" in any cluster. Our work builds on the work of Chierichetti et al. (NIPS 2017), Bera et al. (NeurIPS 2019), Ahmadian et al. (KDD 2019), and Bercea et al. (APPROX 2019).
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
The increasing impact of black box models, and particularly of unsupervised ones, comes with an increasing interest in tools to understand and interpret them.
Fair Clustering Under a Bounded Cost
Clustering is a fundamental unsupervised learning problem where a dataset is partitioned into clusters that consist of nearby points in a metric space.
Probabilistic Fair Clustering
We also address the problem of "metric membership", where group membership has a notion of order and distance.
