Silvio Lattanzi
Silvio received his bachelor (2005), master (2007) and PhD(2011) degree from the Computer Science department of Sapienza University of Rome, under the supervision of Alessandro Panconesi. Silvio joined Google Research in the New York office in January 2011. Since April 2017 Silvio moved to Google Research Zurich.
Authored Publications
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Learning graph cluster structure using few queries is a classical question in property testing, with the fundamental special case, namely expansion testing, considered in the seminal work of Goldreich and Ron[STOC'96].
The most recent result in this line of work, due to Gluch et al.[SODA'21], designs {\em clustering oracles} for $(k, \e)$-clusterable graphs. These oracles, given a graph whose vertex set can be partitioned into a disjoint union of $k$ clusters (i.e., good expanders) with outer conductances bounded by $\e\ll 1$, provide query access to an $O(\e \log k)$-approximation to this ground truth clustering in time $\approx 2^{\text{poly}(k/\e)} n^{1/2+O(\e)}$ per query.
In this paper we show that it is possible to learn the {\em hierarchical} cluster structure of $(k, \e)$-clusterable graphs in sublinear time. First, we show how to simulate the hierarchical clustering algorithm of Charikar-Chatziafratis[SODA'17] to approximate the Dasgupta cost of a $k$-clusterable graph to within a factor of $O(\sqrt{\log k})$ in $\approx \text{poly}(k)\cdot n^{1/2+O(\e)}$ time assuming oracle access to the clustering. Second, we introduce a natural hierarchical model of clusterable graphs, and give a bona fide clustering oracle for this model, i.e. a small space data structure that can answer hierarchical clustering queries in $\approx\text{poly}(k) \cdot n^{1/2+O(\e)}$ time per query. Notably, in both cases the query time depends on polynomially on the number $k$ of clusters. The second result is the main technical contribution of the paper, and relies on several structural properties of hierarchically clusterable graphs that we hope will be of independent interest in sublinear time spectral graph algorithms.
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Designing algorithms for machine learning problems beyond worst-case analysis and, in particular,analyzing the effect of side-information on the complexity of such problems is a very important line of research with many practical applications. In this paper we study the classic k-means clustering problem in the presence of noisy labels. In this problem we receive as input a set of points and a set of clustering labels generated by either an adversarial or random perturbation of the optimal solution. Our main goal is to formally study the effect of this extra information on the complexity of the k-means problem. In particular, in the context of random perturbations, we give an efficient algorithm that finds a clustering of cost within a factor 1 +o(1)of optimum even when the label of each point is perturbed with a large probability (think 99%). In contrast, we show that side-information with adversarial perturbations is as hard as the original problem even if only a small fraction of the labels are perturbed. We complement this negative result by giving a simple algorithm in the case when the adversary is only allowed to perturb anfraction of each cluster.
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In the correlation clustering problem the input is a signed graph where the sign indicates whether each pair of points should be placed in the same cluster or not. The goal of the problem is to compute a clustering which minimizes the number of disagreements with such recommendation. Thanks to its many practical applications, correlation clustering is a fundamental unsupervised learning problem and has been extensively studied in many different settings. In this paper we study the problem in the classic online setting with recourse; The vertices of the graphs arrive in an online manner and the goal is to maintain an approximate clustering while minimizing the number of times each vertex changes cluster.
Our main contribution is an algorithm that achieves logarithmic recourse per vertex in the worst case. We also complement this result with a tight lower bound. Finally we show experimentally that our algorithm achieves better performances than state-of-the-art algorithms on real world data.
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Thanks to its many applications the clustering ensemble problem has been extensively studied in the past. In htis problem we are giving in input $m$ clustering and the objective is to output a clustering that ``well-represent'' all the input clustering. In this paper, we propose to thee best of our knowledge the first generative model for the problem. Our model is parameterized by a ``center'' clustering and a scale; the probability of a particular clustering is an exponential function of its Rand distance to the center, scaled.
For this new model, we show: (i) a sampling algorithm that runs in polynomial time when the center has a constant number of clusters and (ii) a simple polynomial time reconstruction algorithm when the scale is small.
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Correlation Clustering in Constant Many Parallel Rounds
Ashkan Norouzi Fard
Jakub Tarnawski
Slobodan Mitrović
ICML(2022) (to appear)
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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 and the goal is to partition it to minimize the number of disagreements. In this work we propose a massively parallel computation (MPC) algorithm for this problem that is considerably faster than prior work. In particular, our algorithm uses machines with memory sublinear in the number of nodes in the graph and returns a constant approximation while running only for a constant number of rounds. To the best of our knowledge, our algorithm is the first that can provably approximate a clustering problem using only a constant number of MPC rounds in the sublinear memory regime. We complement our analysis with an experimental scalability\nnote{I would remove "scalability": it is not clear that this will be demonstrated with mid-sized graphs} evaluation of our techniques.
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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 a stable and high quality solution throughout the data stream while using only little time per update (insertion or deletion). We study the problem and provide the first algorithm that at the same time maintains a constant approximation and only uses near-linear time per update and incurs in polylogarithmic recourse per update. We complement our theoretical results with an experimental analysis showing the practical efficiency of our method.
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Deletion Robust Submodular Maximization over Matroids
Ashkan Norouzi Fard
Federico Fusco
Paul Duetting
ICML'22(2022)
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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 constraint. Here the goal is to extract a small size summary of the dataset that contains a high value independent set even after an adversary deleted some elements. We present constant-factor approximation algorithms, whose space complexity depends on the rank $k$ of the matroid and the number $d$ of deleted elements. In the centralized setting we present a $(3.582+O(\eps))$-approximation algorithm with summary size $O(k + \frac{d \log k}{\eps^2})$. In the streaming setting we provide a $(5.582+O(\eps))$-approximation algorithm with summary size and memory $O(k + \frac{d \log k}{\eps^2})$. We complement our theoretical results with an in-depth experimental analysis showing the effectiveness of our algorithms on real-world datasets.
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Scalable Differentially Private Clustering via Hierarchically Separated Trees
Chris Schwiegelshohn
David Saulpic
2022 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(2022) (to appear)
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We study the private $k$-median and $k$-means clustering problem in $d$ dimensional Euclidean space.
By leveraging tree embeddings, we give an efficient and easy to implement algorithm, that is empirically competitive with state of the art non private methods.
We prove that our method computes a solution with cost at most $O(d^{3/2}\log n)\cdot OPT + O(k d^2 \log^2 n / \epsilon^2)$, where $\epsilon$ is the privacy guarantee. (The dimension term, $d$, can be replaced with $O(\log k)$ using standard dimension reduction techniques.) Although the worst-case guarantee is worse than that of state of the art private clustering methods, the algorithm we propose is practical, runs in near-linear, $\tilde{O}(nkd)$, time and scales to tens of millions of points. We also show that our method is amenable to parallelization in large-scale distributed computing environments. In particular we show that our private algorithms can be implemented in logarithmic number of MPC rounds in the sublinear memory regime.
Finally, we complement our theoretical analysis with an empirical evaluation demonstrating the algorithm's efficiency and accuracy in comparison to other privacy clustering baselines.
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Near-Optimal Correlation Clustering with Privacy
Ashkan Norouzi Fard
Chenglin Fan
Jakub Tarnawski
Slobodan Mitrović
NeurIPS 2022(2022) (to appear)
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Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labeling and many more. In the correlation clustering problem one receives as input a set of nodes and for each node a list of co-clustering preferences, and the goal is to output a clustering that minimizes the disagreement with the specified nodes' preferences. In this paper, we introduce a simple and computationally efficient algorithm for the correlation clustering problem with provable privacy guarantees. Our additive error is stronger than the one shown in prior work and is optimal up to polylogarithmic factors for fixed privacy parameters.
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Active Learning of Classifiers with Label and Seed Queries
Andrea Paudice
Marco Bressan
Maximilian Thiessen
Nicolo Cesa-Bianchi
NeurIPS 2022 (to appear)
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We study exact active learning of binary and multiclass classifiers with margin. Given an $n$-point set $X \subset \R^m$, we want to learn any unknown classifier on $X$ whose classes have finite \emph{strong convex hull margin}, a new notion extending the SVM margin.
On the other hand, using the more powerful \emph{seed} queries (a variant of equivalence queries), the target classifier could be learned in $\scO(m \log n)$ queries via Littlestone's Halving algorithm; however, Halving is computationally inefficient.
In this work we show that, by carefully combining the two types of queries, a binary classifier can be learned in time $\poly(n+m)$ using only $\scO(m^2 \log n)$ label queries and $\scO\big(m \log \frac{m}{\gamma}\big)$ seed queries; the result extends to $k$-class classifiers at the price of a $k!k^2$ multiplicative overhead. Similar results hold when the input points have bounded bit complexity, or when only one class has strong convex hull margin against the rest. We complement these upper bounds by showing that in the worst case any algorithm needs $\Omega\big(\frac{k m \log \nicefrac{1}{\gamma}}{\log m}\big)$ seed and label queries to learn a $k$-class classifier with strong convex hull margin $\gamma$.
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