activity
20202022
most citedFair Hierarchical Clustering

21 citations · 21 across the 4 of their papers we have counts for

collaborators

5 papers

cs.DS2022

Adaptive Massively Parallel Algorithms for Cut Problems

MohammadTaghi Hajiaghayi, Marina Knittel, Jan Olkowski +1

We study the Weighted Min Cut problem in the Adaptive Massively Parallel Computation (AMPC) model. In 2019, Behnezhad et al. [3] introduced the AMPC model as an extension of the Ma…

cs.GT2022

The Dichotomous Affiliate Stable Matching Problem: Approval-Based Matching with Applicant-Employer Relations

Marina Knittel, Samuel Dooley, John P. Dickerson

While the stable marriage problem and its variants model a vast range of matching markets, they fail to capture complex agent relationships, such as the affiliation of applicants a…

cs.DS2021

Adaptive Massively Parallel Constant-round Tree Contraction

MohammadTaghi Hajiaghayi, Marina Knittel, Hamed Saleh +1

Miller and Reif's FOCS'85 classic and fundamental tree contraction algorithm is a broadly applicable technique for the parallel solution of a large number of tree problems. Additio…

cs.DS2021

Improved Hierarchical Clustering on Massive Datasets with Broad Guarantees

MohammadTaghi Hajiaghayi, Marina Knittel

Hierarchical clustering is a stronger extension of one of today's most influential unsupervised learning methods: clustering. The goal of this method is to create a hierarchy of cl…

cs.DS202021 cited

Fair Hierarchical Clustering

Sara Ahmadian, Alessandro Epasto, Marina Knittel +6

As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair. Recently, there has been an interest…