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Found 5 papers, 1 papers with code
Fair k-Centers via Maximum Matching
The field of algorithms has seen a push for fairness, or the removal of inherent bias, in recent history.
Improved Learning-augmented Algorithms for k-means and k-medians Clustering
We consider the problem of clustering in the learning-augmented setting, where we are given a data set in $d$-dimensional Euclidean space, and a label for each data point given by an oracle indicating what subsets of points should be clustered together.
Streaming Submodular Maximization with Differential Privacy
In this work, we study the problem of privately maximizing a submodular function in the streaming setting.
An Efficient Algorithm for Fair Multi-Agent Multi-Armed Bandit with Low Regret
This is an improvement to the previous approach, which has regret bound of $\tilde{O}( \min(NK, \sqrt{N} K^{3/2})\sqrt{T})$.
Differentially Private Clustering via Maximum Coverage
This paper studies the problem of clustering in metric spaces while preserving the privacy of individual data.
