Search Results for author: Silvio Lattanzi

Found 28 papers, 4 papers with code

Near-Optimal Correlation Clustering with Privacy

no code implementations2 Mar 2022 Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski

Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labelling and many more.

Community Detection

Efficient and Local Parallel Random Walks

no code implementations NeurIPS 2021 Michael Kapralov, Silvio Lattanzi, Navid Nouri, Jakab Tardos

Random walks are a fundamental primitive used in many machine learning algorithms with several applications in clustering and semi-supervised learning.

Parallel and Efficient Hierarchical k-Median Clustering

no code implementations NeurIPS 2021 Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson

In this paper we introduce a new parallel algorithm for the Euclidean hierarchical $k$-median problem that, when using machines with memory $s$ (for $s\in \Omega(\log^2 (n+\Delta+d))$), outputs a hierarchical clustering such that for every fixed value of $k$ the cost of the solution is at most an $O(\min\{d, \log n\} \log \Delta)$ factor larger in expectation than that of an optimal solution.

Robust Online Correlation Clustering

no code implementations NeurIPS 2021 Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang, Rudy Zhou

In correlation clustering we are given a set of points along with recommendations whether each pair of points should be placed in the same cluster or into separate clusters.

Online Facility Location with Multiple Advice

no code implementations NeurIPS 2021 Matteo Almanza, Flavio Chierichetti, Silvio Lattanzi, Alessandro Panconesi, Giuseppe Re

Clustering is a central topic in unsupervised learning and its online formulation has received a lot of attention in recent years.

Correlation Clustering in Constant Many Parallel Rounds

no code implementations15 Jun 2021 Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski

Correlation clustering is a central topic in unsupervised learning, with many applications in ML and data mining.

On Margin-Based Cluster Recovery with Oracle Queries

no code implementations NeurIPS 2021 Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice

We study an active cluster recovery problem where, given a set of $n$ points and an oracle answering queries like "are these two points in the same cluster?

Exact Recovery of Clusters in Finite Metric Spaces Using Oracle Queries

no code implementations31 Jan 2021 Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice

Previous results show that clusters in Euclidean spaces that are convex and separated with a margin can be reconstructed exactly using only $O(\log n)$ same-cluster queries, where $n$ is the number of input points.

Spectral Clustering Oracles in Sublinear Time

no code implementations14 Jan 2021 Grzegorz Gluch, Michael Kapralov, Silvio Lattanzi, Aida Mousavifar, Christian Sohler

The main technical contribution is a sublinear time oracle that provides dot product access to the spectral embedding of $G$ by estimating distributions of short random walks from vertices in $G$.

Data Structures and Algorithms

Fast and Accurate $k$-means++ via Rejection Sampling

no code implementations NeurIPS 2020 Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson

$k$-means++ \cite{arthur2007k} is a widely used clustering algorithm that is easy to implement, has nice theoretical guarantees and strong empirical performance.

Online MAP Inference of Determinantal Point Processes

no code implementations NeurIPS 2020 Aditya Bhaskara, Amin Karbasi, Silvio Lattanzi, Morteza Zadimoghaddam

In this paper, we provide an efficient approximation algorithm for finding the most likelihood configuration (MAP) of size $k$ for Determinantal Point Processes (DPP) in the online setting where the data points arrive in an arbitrary order and the algorithm cannot discard the selected elements from its local memory.

Point Processes

On Mean Estimation for Heteroscedastic Random Variables

no code implementations22 Oct 2020 Luc Devroye, Silvio Lattanzi, Gabor Lugosi, Nikita Zhivotovskiy

We study the problem of estimating the common mean $\mu$ of $n$ independent symmetric random variables with different and unknown standard deviations $\sigma_1 \le \sigma_2 \le \cdots \le\sigma_n$.

InstantEmbedding: Efficient Local Node Representations

no code implementations14 Oct 2020 Ştefan Postăvaru, Anton Tsitsulin, Filipe Miguel Gonçalves de Almeida, Yingtao Tian, Silvio Lattanzi, Bryan Perozzi

In this paper, we introduce InstantEmbedding, an efficient method for generating single-node representations using local PageRank computations.

Link Prediction Node Classification +1

Sliding Window Algorithms for k-Clustering Problems

1 code implementation NeurIPS 2020 Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam

The sliding window model of computation captures scenarios in which data is arriving continuously, but only the latest $w$ elements should be used for analysis.

Data Structures and Algorithms

Exact Recovery of Mangled Clusters with Same-Cluster Queries

no code implementations NeurIPS 2020 Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice

Given a finite set of input points, and an oracle revealing whether any two points lie in the same cluster, our goal is to recover all clusters exactly using as few queries as possible.

Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity

no code implementations2 May 2019 Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam, Silvio Lattanzi, Amin Karbasi

We show how one can achieve the tight $(1/2)$-approximation guarantee with $O(k)$ shared memory while minimizing not only the required rounds of computations but also the total number of communicated bits.

Data Summarization

Mallows Models for Top-k Lists

no code implementations NeurIPS 2018 Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan, Ravi Kumar, Silvio Lattanzi

The classic Mallows model is a widely-used tool to realize distributions on per- mutations.

Parallel and Streaming Algorithms for K-Core Decomposition

no code implementations ICML 2018 Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni

The $k$-core decomposition is a fundamental primitive in many machine learning and data mining applications.

Fair Clustering Through Fairlets

2 code implementations NeurIPS 2017 Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii

We show that any fair clustering problem can be decomposed into first finding good fairlets, and then using existing machinery for traditional clustering algorithms.

Algorithms for $\ell_p$ Low-Rank Approximation

no code implementations ICML 2017 Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, Rina Panigrahy, David P. Woodruff

We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the entrywise $\ell_p$-approximation error, for any $p \geq 1$; the case $p = 2$ is the classical SVD problem.

Consistent k-Clustering

no code implementations ICML 2017 Silvio Lattanzi, Sergei Vassilvitskii

The study of online algorithms and competitive analysis provides a solid foundation for studying the quality of irrevocable decision making when the data arrives in an online manner.

Decision Making

Community Detection on Evolving Graphs

no code implementations NeurIPS 2016 Aris Anagnostopoulos, Jakub Łącki, Silvio Lattanzi, Stefano Leonardi, Mohammad Mahdian

In many of these applications, the input graph evolves over time in a continual and decentralized manner, and, to maintain a good clustering, the clustering algorithm needs to repeatedly probe the graph.

Community Detection Information Retrieval +1

Distributed Balanced Clustering via Mapping Coresets

no code implementations NeurIPS 2014 Mohammadhossein Bateni, Aditya Bhaskara, Silvio Lattanzi, Vahab Mirrokni

Large-scale clustering of data points in metric spaces is an important problem in mining big data sets.

Local Graph Clustering Beyond Cheeger's Inequality

no code implementations30 Apr 2013 Zeyuan Allen Zhu, Silvio Lattanzi, Vahab Mirrokni

We also prove that our analysis is tight, and perform empirical evaluation to support our theory on both synthetic and real data.

Graph Clustering

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