32 citations · 140 across the 32 of their papers we have counts for
38 papers
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…
Efficient Dynamic Algorithms for Graph Neural Networks with Non-Linear Propagation
Kiarash Banihashem, MohammadTaghi Hajiaghayi, Mahdi JafariRaviz +2
Graph Neural Networks (GNNs) are widely used for representation learning on graphs, but most methods assume static topologies, making them inefficient on evolving networks where ed…
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…
On Classification Thresholds for Graph Attention with Edge Features
Kimon Fountoulakis, Dake He, Silvio Lattanzi +3
The recent years we have seen the rise of graph neural networks for prediction tasks on graphs. One of the dominant architectures is graph attention due to its ability to make pred…
Active Learning of Classifiers with Label and Seed Queries
Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi +2
We study exact active learning of binary and multiclass classifiers with margin. Given an -point set , we want to learn any unknown classifier on who…
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…
TF-GNN: Graph Neural Networks in TensorFlow
Oleksandr Ferludin, Arno Eigenwillig, Martin Blais +24
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that…
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…
Near-Optimal Correlation Clustering with Privacy
Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi +4
Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labelling and many more. In the…
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…
Yang-Mills-Scalar-Matter Fields in the Two-Point Space
Gustavo Amilcar Saldaña Moncada
The purpose of this work is to present a Non--Commutative Geometrical version of the Yang--Mills Theory and the Yang--Mills Scalar Matter Theory by constructing a concrete example…
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…
On Margin-Based Cluster Recovery with Oracle Queries
Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi +1
We study an active cluster recovery problem where, given a set of points and an oracle answering queries like "are these two points in the same cluster?", the task is to recove…
Exact Recovery of Clusters in Finite Metric Spaces Using Oracle Queries
Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi +1
We investigate the problem of exact cluster recovery using oracle queries. Previous results show that clusters in Euclidean spaces that are convex and separated with a margin can b…
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…
Fast and Accurate -means++ via Rejection Sampling
Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard +2
-means++ \cite{arthur2007k} is a widely used clustering algorithm that is easy to implement, has nice theoretical guarantees and strong empirical performance. Despite its wide a…
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…
On Mean Estimation for Heteroscedastic Random Variables
Luc Devroye, Silvio Lattanzi, Gabor Lugosi +1
We study the problem of estimating the common mean of independent symmetric random variables with different and unknown standard deviations .…
InstantEmbedding: Efficient Local Node Representations
Ştefan Postăvaru, Anton Tsitsulin, Filipe Miguel Gonçalves de Almeida +3
In this paper, we introduce InstantEmbedding, an efficient method for generating single-node representations using local PageRank computations. We theoretically prove that our appr…
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…