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publications
Dynamic Algorithm for Explainable \(k\)-medians Clustering under the \(l_p\) Norm
Konstantin Makarychev, Ilias Papanikolaou, Liren Shan
Published in NeurIPS (Spotlight Presentation), 2025
We study the problem of explainable \(k\)-medians clustering introduced by Dasgupta, Frost, Moshkovitz, and Rashtchian (2020). In this problem, the goal is to construct a threshold decision tree that partitions data into \(k\) clusters while minimizing the \(k\)-medians objective. These trees are interpretable because each internal node makes a simple decision by thresholding a single feature, allowing users to trace and understand how each point is assigned to a cluster. We design an accurate static explainable clustering algorithm for the \(k\)-medians objective under the \(\ell_p\) norm for every \(p \geq 1\) and then show how to implement it in the dynamic setting, where the input is gradually revealed to the algorithm.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Lab Assistant for the course “Computer Programming”
Undergraduate course, National Technical University of Athens, 2022
Teaching Assistant for the course “Algorithms and Complexity”
Undergraduate course, National Technical University of Athens, 2022
Teaching Assistant for CS212 - “Discrete Math”
Undergraduate course, Northwestern University, 2025