Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Bandyapadhyay, Sayan"'
Fairness in multi-document summarization of user-generated content remains a critical challenge in natural language processing (NLP). Existing summarization methods often fail to ensure equitable representation across different social groups, leading
Externí odkaz:
http://arxiv.org/abs/2411.07521
In this work, we study pairwise fair clustering with $\ell \ge 2$ groups, where for every cluster $C$ and every group $i \in [\ell]$, the number of points in $C$ from group $i$ must be at most $t$ times the number of points in $C$ from any other grou
Externí odkaz:
http://arxiv.org/abs/2405.10378
Autor:
Bandyapadhyay, Sayan, Xue, Jie
Geometric matching is an important topic in computational geometry and has been extensively studied over decades. In this paper, we study a geometric-matching problem, known as geometric many-to-many matching. In this problem, the input is a set $S$
Externí odkaz:
http://arxiv.org/abs/2402.15837
In the Euclidean Bottleneck Steiner Tree problem, the input consists of a set of $n$ points in $\mathbb{R}^2$ called terminals and a parameter $k$, and the goal is to compute a Steiner tree that spans all the terminals and contains at most $k$ points
Externí odkaz:
http://arxiv.org/abs/2312.01589
In this paper, we study two generalizations of Vertex Cover and Edge Cover, namely Colorful Vertex Cover and Colorful Edge Cover. In the Colorful Vertex Cover problem, given an $n$-vertex edge-colored graph $G$ with colors from $\{1, \ldots, \omega\}
Externí odkaz:
http://arxiv.org/abs/2308.15842
We revisit a natural variant of geometric set cover, called minimum-membership geometric set cover (MMGSC). In this problem, the input consists of a set $S$ of points and a set $\mathcal{R}$ of geometric objects, and the goal is to find a subset $\ma
Externí odkaz:
http://arxiv.org/abs/2305.03985
Clustering with capacity constraints is a fundamental problem that attracted significant attention throughout the years. In this paper, we give the first FPT constant-factor approximation algorithm for the problem of clustering points in a general me
Externí odkaz:
http://arxiv.org/abs/2303.07923
Designing coresets--small-space sketches of the data preserving cost of the solutions within $(1\pm \epsilon)$-approximate factor--is an important research direction in the study of center-based $k$-clustering problems, such as $k$-means or $k$-media
Externí odkaz:
http://arxiv.org/abs/2303.01400
The study of fair algorithms has become mainstream in machine learning and artificial intelligence due to its increasing demand in dealing with biases and discrimination. Along this line, researchers have considered fair versions of traditional optim
Externí odkaz:
http://arxiv.org/abs/2301.03862
$k$-center is one of the most popular clustering models. While it admits a simple 2-approximation in polynomial time in general metrics, the Euclidean version is NP-hard to approximate within a factor of 1.93, even in the plane, if one insists the de
Externí odkaz:
http://arxiv.org/abs/2112.10195