21 citations · 23 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
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…