32 citations · 140 across the 32 of their papers we have counts for
21 papers · 1 filter
Constant-Factor Local Approximation for Bipartite Densest Subgraph
Michael Kapralov, Jakub Łącki, Silvio Lattanzi +1
Given a vertex in a large bipartite graph , can one quickly find a small densely connected region near ? This is the algorithmic core of many fraud detection, community d…
Almost Tight Bounds for Differentially Private Densest Subgraph
Michael Dinitz, Satyen Kale, Silvio Lattanzi +1
We study the Densest Subgraph (DSG) problem under the additional constraint of differential privacy. DSG is a fundamental theoretical question which plays a central role in graph a…
Fully Dynamic Submodular Maximization over Matroids
Paul Dütting, Federico Fusco, Silvio Lattanzi +2
Maximizing monotone submodular functions under a matroid constraint is a classic algorithmic problem with multiple applications in data mining and machine learning. We study this c…
Efficient and Stable Fully Dynamic Facility Location
Sayan Bhattacharya, Silvio Lattanzi, Nikos Parotsidis
We consider the classic facility location problem in fully dynamic data streams, where elements can be both inserted and deleted. In this problem, one is interested in maintaining…
Deletion Robust Non-Monotone Submodular Maximization over Matroids
Paul Dütting, Federico Fusco, Silvio Lattanzi +2
Maximizing a submodular function is a fundamental task in machine learning and in this paper we study the deletion robust version of the problem under the classic matroids constrai…
Approximating Dasgupta Cost in Sublinear Time from a Few Random Seeds
Michael Kapralov, Akash Kumar, Silvio Lattanzi +2
Testing graph cluster structure has been a central object of study in property testing since the foundational work of Goldreich and Ron [STOC'96] on expansion testing, i.e. the pro…
Scalable Differentially Private Clustering via Hierarchically Separated Trees
Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi +5
We study the private -median and -means clustering problem in dimensional Euclidean space. By leveraging tree embeddings, we give an efficient and easy to implement algor…
Deletion Robust Submodular Maximization over Matroids
Paul Dütting, Federico Fusco, Silvio Lattanzi +2
Maximizing a monotone submodular function is a fundamental task in machine learning. In this paper, we study the deletion robust version of the problem under the classic matroids c…
Correlation Clustering in Constant Many Parallel Rounds
Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrović +3
Correlation clustering is a central topic in unsupervised learning, with many applications in ML and data mining. In correlation clustering, one receives as input a signed graph an…
Spectral Clustering Oracles in Sublinear Time
Grzegorz Gluch, Michael Kapralov, Silvio Lattanzi +2
Given a graph that can be partitioned into disjoint expanders with outer conductance upper bounded by , can we efficiently construct a small space data structure th…
Consistent k-Clustering for General Metrics
Hendrik Fichtenberger, Silvio Lattanzi, Ashkan Norouzi-Fard +1
Given a stream of points in a metric space, is it possible to maintain a constant approximate clustering by changing the cluster centers only a small number of times during the ent…
Secretaries with Advice
Paul Dütting, Silvio Lattanzi, Renato Paes Leme +1
The secretary problem is probably the purest model of decision making under uncertainty. In this paper we ask which advice can we give the algorithm to improve its success probabil…
Sliding Window Algorithms for k-Clustering Problems
Michele Borassi, Alessandro Epasto, Silvio Lattanzi +2
The sliding window model of computation captures scenarios in which data is arriving continuously, but only the latest elements should be used for analysis. The goal is to desi…
Fully Dynamic Algorithm for Constrained Submodular Optimization
Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard +2
The task of maximizing a monotone submodular function under a cardinality constraint is at the core of many machine learning and data mining applications, including data summarizat…
MapReduce Meets Fine-Grained Complexity: MapReduce Algorithms for APSP, Matrix Multiplication, 3-SUM, and Beyond
MohammadTaghi Hajiaghayi, Silvio Lattanzi, Saeed Seddighin +1
Distributed processing frameworks, such as MapReduce, Hadoop, and Spark are popular systems for processing large amounts of data. The design of efficient algorithms in these framew…
Parallel and Streaming Algorithms for K-Core Decomposition
Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni
The -core decomposition is a fundamental primitive in many machine learning and data mining applications. We present the first distributed and the first streaming algorithms to…
Algorithms for Low Rank Approximation
Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar +3
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the entrywise -approximation error, for any ; the case is t…
Submodular Optimization over Sliding Windows
Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii +1
Maximizing submodular functions under cardinality constraints lies at the core of numerous data mining and machine learning applications, including data diversification, data summa…
Expanders via Local Edge Flips
Zeyuan Allen-Zhu, Aditya Bhaskara, Silvio Lattanzi +2
Designing distributed and scalable algorithms to improve network connectivity is a central topic in peer-to-peer networks. In this paper we focus on the following well-known proble…
An efficient reconciliation algorithm for social networks
Nitish Korula, Silvio Lattanzi
People today typically use multiple online social networks (Facebook, Twitter, Google+, LinkedIn, etc.). Each online network represents a subset of their "real" ego-networks. An in…
Local Graph Clustering Beyond Cheeger's Inequality
Zeyuan Allen Zhu, Silvio Lattanzi, Vahab Mirrokni
Motivated by applications of large-scale graph clustering, we study random-walk-based LOCAL algorithms whose running times depend only on the size of the output cluster, rather tha…