15 accepted papers
Given a non-negative integer \ell, the k-median with outliers problem extends the standard k-median problem by allowing the removal of up to \ell points and minimizing the clustering cost over the remaining ones. Algorithmic development in this setting remains an active area of research due to its r
Fair clustering has attracted increased attention in recent years. In this work, we study the individually fair clustering problem in Euclidean space. While single-swap local search methods have achieved near-linear running time and constant approximation guarantees, their performance often depends
In this paper, we study the fully-dynamic k-center with outliers problem. In this setting, the clustering data changes over time through a sequence of updates. The goal is to maintain an approximate k-center solution with efficient update and query time, while allowing up to z points to be discarded…
In this paper, we study coreset construction for LASSO regression, where a coreset is a small, weighted subset of the data that approximates the original problem with provable guarantees. For unregularized regression problems, sensitivity sampling is a successful and widely applied technique for con…
Clustering is a fundamental problem that has been extensively studied over past few decades, with most research focusing on point-based clustering such as $k$-means, $k$-median, and $k$-center. However, numerous real-world applications, such as motion analysis, computer vision, and missing data anal…
Local search is a powerful clustering technique that provides high-quality solutions with theoretical guarantees. With distance-based sampling strategies, local search methods can achieve constant approximations for clustering with linear running time in data size. Despite their effectiveness, exist…
In this paper, we consider the k-center problem with outliers (the (k, z)-center problem) in the context of Massively Parallel Computation (MPC). Existing MPC algorithms for the (k, z)-center problem typically require Ω(k) local space per machine. While this may be feasible when k is small, these al…
In this paper, we study the clustering problems in the learning-augmented setting, where predicted labels for a d-dimensional dataset with size m are given by an oracle to serve as auxiliary information to improve the clustering performance. Following the prior work, the given oracle is parameterize…
Fair clustering has recently received considerable attention where numerous distinct fairness notions are developed. Despite being well-justified, these fairness notions are frequently studied in isolation, leaving the need to explore how they can be combined. Building on prior work, we focus on the
The k-means with outliers problem is one of the most extensively studied clustering problems in the field of machine learning, where the goal is to discard up to z outliers and identify a minimum k-means clustering on the remaining data points. Most previous results for this problem have running tim…
Fair-range clustering extends classical clustering formulations by associating each data point with one or more demographic labels. It imposes lower and upper bound constraints on the number of facilities opened for each label, ensuring fair representation of all demographic groups by the selected f…
As one of the most popular machine learning tools in the field of unsupervised learning, clustering has been widely used in various practical applications. While numerous methods have been proposed for clustering, a commonly encountered issue is that the existing clustering methods rely heavily on l…
In this paper, we study the $k$-clustering problems with outliers in distributed setting. The current best results for the distributed $k$-center problem with outliers have quadratic local running time with communication cost dependent on the aspect ratio $\Delta$ of the given instance, which may co…
The local search methods have been widely used to solve the clustering problems. In practice, local search algorithms for clustering problems mainly adapt the single-swap strategy, which enables them to handle large-scale datasets and achieve linear running time in the data size. However, compared w…
The k-means problem is an extensively studied unsupervised learning problem with various applications in decision making and data mining. In this paper, we propose a fast and practical local search algorithm for the k-means problem. Our method reduces the search space of swap pairs from O(nk) to O(k…