2.5k citations
- IBM Research - Thomas J. Watson Research CenterUS46 papers
- Massachusetts Institute of TechnologyUS22 papers
- Vassar CollegeUS16 papers
- California Institute of TechnologyUS13 papers
- Columbia UniversityUS12 papers
- Stanford UniversityUS11 papers
- Centre National de la Recherche ScientifiqueFR10 papers
- Carnegie Mellon UniversityUS9 papers
- University of Illinois Urbana-ChampaignUS9 papers
- University of MichiganUS9 papers
- IBM Research - ZurichCH8 papers
- Iowa State UniversityUS8 papers
44 papers · 1 filter
Scalable Hierarchical Clustering with Tree Grafting
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy +2
We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets. Th…
A Fluid Limit for Processor-Sharing Queues Weighted by Functions of Remaining Amounts of Service
Yingdong Lu
We study a single server queue under a processor-sharing type of scheduling policy, where the weights for determining the sharing are given by functions of each job's remaining ser…
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
Lingfei Wu, Ian En-Hsu Yen, Siyu Huo +5
Analysis of large-scale sequential data has been one of the most crucial tasks in areas such as bioinformatics, text, and audio mining. Existing string kernels, however, either (i)…
KerGM: Kernelized Graph Matching
Zhen Zhang, Yijian Xiang, Lingfei Wu +2
Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics. Graph matching problems can be cast as two types of quadratic assign…
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang +5
Graph kernels are widely used for measuring the similarity between graphs. Many existing graph kernels, which focus on local patterns within graphs rather than their global propert…
SySCD: A System-Aware Parallel Coordinate Descent Algorithm
Nikolas Ioannou, Celestine Mendler-Dünner, Thomas Parnell
In this paper we propose a novel parallel stochastic coordinate descent (SCD) algorithm with convergence guarantees that exhibits strong scalability. We start by studying a state-o…