
Exact Recovery of Clusters in Finite Metric Spaces Using Oracle Queries
We investigate the problem of exact cluster recovery using oracle querie...
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Spectral Clustering Oracles in Sublinear Time
Given a graph G that can be partitioned into k disjoint expanders with o...
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Fast and Accurate kmeans++ via Rejection Sampling
kmeans++ <cit.> is a widely used clustering algorithm that is easy to i...
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Consistent kClustering for General Metrics
Given a stream of points in a metric space, is it possible to maintain a...
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Secretaries with Advice
The secretary problem is probably the purest model of decision making un...
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On Mean Estimation for Heteroscedastic Random Variables
We study the problem of estimating the common mean μ of n independent sy...
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InstantEmbedding: Efficient Local Node Representations
In this paper, we introduce InstantEmbedding, an efficient method for ge...
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Sliding Window Algorithms for kClustering Problems
The sliding window model of computation captures scenarios in which data...
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Fully Dynamic Algorithm for Constrained Submodular Optimization
The task of maximizing a monotone submodular function under a cardinalit...
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Exact Recovery of Mangled Clusters with SameCluster Queries
We study the problem of recovering distorted clusters in the semisuperv...
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Dynamic Algorithms for the Massively Parallel Computation Model
The Massive Parallel Computing (MPC) model gained popularity during the ...
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MapReduce Meets FineGrained Complexity: MapReduce Algorithms for APSP, Matrix Multiplication, 3SUM, and Beyond
Distributed processing frameworks, such as MapReduce, Hadoop, and Spark ...
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Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity
Streaming algorithms are generally judged by the quality of their soluti...
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Parallel and Streaming Algorithms for KCore Decomposition
The kcore decomposition is a fundamental primitive in many machine lear...
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Fair Clustering Through Fairlets
We study the question of fair clustering under the disparate impact doc...
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ASYMP: Faulttolerant Mining of Massive Graphs
We present ASYMP, a distributed graph processing system developed for th...
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OneShot Coresets: The Case of kClustering
Scaling clustering algorithms to massive data sets is a challenging task...
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Local Graph Clustering Beyond Cheeger's Inequality
Motivated by applications of largescale graph clustering, we study rand...
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Silvio Lattanzi
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