Mining Top-K Frequent Itemsets Through Progressive Sampling - Andrea Pietracaprina; Matteo Riondato; Eli Upfal; Fabio Vandin (CoRR, 2010)

Mining top-K frequent itemsets through progressive sampling. - Andrea Pietracaprina; Matteo Riondato; Eli Upfal; Fabio Vandin (Data Min. Knowl. Discov., 2010)

The Case for Predictive Database Systems: Opportunities and Challenges. - Mert Akdere; Ugur Çetintemel; Matteo Riondato; Eli Upfal; Stanley B. Zdonik (CIDR, 2011)

The VC-Dimension of SQL Queries and Selectivity Estimation through Sampling. - Matteo Riondato; Mert Akdere; Ugur Çetintemel; Stanley B. Zdonik; Eli Upfal (ECML/PKDD (2), 2011)

The VC-Dimension of Queries and Selectivity Estimation Through Sampling - Matteo Riondato; Mert Akdere; Ugur Çetintemel; Stanley B. Zdonik; Eli Upfal (CoRR, 2011)

PARMA: a parallel randomized algorithm for approximate association rules mining in MapReduce. - Matteo Riondato; Justin A. DeBrabant; Rodrigo Fonseca; Eli Upfal (CIKM, 2012)

Learning-based Query Performance Modeling and Prediction. - Mert Akdere; Ugur Çetintemel; Matteo Riondato; Eli Upfal; Stanley B. Zdonik (ICDE, 2012)

Space-round tradeoffs for MapReduce computations. - Andrea Pietracaprina; Geppino Pucci; Matteo Riondato; Francesco Silvestri 001; Eli Upfal (ICS, 2012)

Controlling False Positives in Frequent Itemsets Mining through the VC-Dimension - Matteo Riondato; Fabio Vandin (CoRR, 2013)

Sampling-Based Data Mining Algorithms: Modern Techniques and Case Studies. - Matteo Riondato (ECML/PKDD (3), 2014)

Finding the True Frequent Itemsets. - Matteo Riondato; Fabio Vandin (SDM, 2014)

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees. - Matteo Riondato; Eli Upfal (ACM Trans. Knowl. Discov. Data, 2014)

Sampling-based Randomized Algorithms for Big Data Analytics. - Matteo Riondato (2014)

Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages. - Matteo Riondato; Eli Upfal (KDD, 2015)

VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms. - Matteo Riondato; Eli Upfal (KDD, 2015)

The Importance of Being Expert: Efficient Max-Finding in Crowdsourcing. - Aris Anagnostopoulos; Luca Becchetti; Adriano Fazzone; Ida Mele; Matteo Riondato (SIGMOD Conference, 2015)

Wiggins: Detecting Valuable Information in Dynamic Networks Using Limited Resources. - Ahmad Mahmoody; Matteo Riondato; Eli Upfal (WSDM, 2016)

Centrality Measures on Big Graphs: Exact, Approximated, and Distributed Algorithms. - Francesco Bonchi; Gianmarco De Francisci Morales; Matteo Riondato (WWW (Companion Volume), 2016)

Fast approximation of betweenness centrality through sampling. - Matteo Riondato; Evgenios M. Kornaropoulos (Data Min. Knowl. Discov., 2016)

Graph summarization with quality guarantees. - Matteo Riondato; David García-Soriano; Francesco Bonchi (Data Min. Knowl. Discov., 2017)

TRIÈST: Counting Local and Global Triangles in Fully Dynamic Streams with Fixed Memory Size. - Lorenzo De Stefani; Alessandro Epasto; Matteo Riondato; Eli Upfal (ACM Trans. Knowl. Discov. Data, 2017)

ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages. - Matteo Riondato; Eli Upfal (ACM Trans. Knowl. Discov. Data, 2018)

SPuManTE: Significant Pattern Mining with Unconditional Testing. - Leonardo Pellegrina; Matteo Riondato; Fabio Vandin (KDD, 2019)

Hypothesis Testing and Statistically-sound Pattern Mining. - Leonardo Pellegrina; Matteo Riondato; Fabio Vandin (KDD, 2019)

CaDET: interpretable parametric conditional density estimation with decision trees and forests. - Cyrus Cousins; Matteo Riondato (Mach. Learn., 2019)

Sharp uniform convergence bounds through empirical centralization. - Cyrus Cousins; Matteo Riondato (NeurIPS, 2020)

ProSecCo: progressive sequence mining with convergence guarantees. - Sacha Servan-Schreiber; Matteo Riondato; Emanuel Zgraggen (Knowl. Inf. Syst., 2020)

MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension. - Matteo Riondato; Fabio Vandin (ACM Trans. Knowl. Discov. Data, 2020)

RePBubLik: Reducing Polarized Bubble Radius with Link Insertions. - Shahrzad Haddadan; Cristina Menghini; Matteo Riondato; Eli Upfal (WSDM, 2021)

TipTap: Approximate Mining of Frequent k-Subgraph Patterns in Evolving Graphs. - Muhammad Anis Uddin Nasir; Çigdem Aslay; Gianmarco De Francisci Morales; Matteo Riondato (ACM Trans. Knowl. Discov. Data, 2021)

A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract). - Alexander W. Lee; Stefan Walzer-Goldfeld; Shukry Zablah; Matteo Riondato (AAAI, 2022)

Reducing polarization and increasing diverse navigability in graphs by inserting edges and swapping edge weights. - Shahrzad Haddadan; Cristina Menghini; Matteo Riondato; Eli Upfal (Data Min. Knowl. Discov., 2022)

SPEck: mining statistically-significant sequential patterns efficiently with exact sampling. - Steedman Jenkins; Stefan Walzer-Goldfeld; Matteo Riondato (Data Min. Knowl. Discov., 2022)

MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining. - Leonardo Pellegrina; Cyrus Cousins; Fabio Vandin; Matteo Riondato (ACM Trans. Knowl. Discov. Data, 2022)

Statistically-Sound Knowledge Discovery from Data: Challenges and Directions. - Matteo Riondato (CogMI, 2023)

Statistically-sound Knowledge Discovery from Data. - Matteo Riondato (SDM, 2023)

An impossibility result for Markov Chain Monte Carlo sampling from micro-canonical bipartite graph ensembles. - Giulia Preti; Gianmarco De Francisci Morales; Matteo Riondato (CoRR, 2023)

ROhAN: Row-order agnostic null models for statistically-sound knowledge discovery. - Maryam Abuissa; Alexander W. Lee; Matteo Riondato (Data Min. Knowl. Discov., 2023)

MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling. - Giulia Preti; Gianmarco De Francisci Morales; Matteo Riondato (ACM Trans. Intell. Syst. Technol., 2023)

Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher Averages. - Cyrus Cousins; Chloe Wohlgemuth; Matteo Riondato (ACM Trans. Knowl. Discov. Data, 2023)

ClaveNet: Generating Afro-Cuban Drum Patterns through Data Augmentation. - Daniel Flores García; Hugo Flores García; Matteo Riondato (Audio Mostly Conference, 2024)

Statistical and Probabilistic Methods in Algorithmic Data Analysis (Dagstuhl Seminar 24391). - Aristides Gionis; Matteo Riondato; Eli Upfal (Dagstuhl Reports, 2024)

Alice and the Caterpillar: A more descriptive null model for assessing data mining results. - Giulia Preti; Gianmarco De Francisci Morales; Matteo Riondato (Knowl. Inf. Syst., 2024)

Polaris: Sampling from the Multigraph Configuration Model with Prescribed Color Assortativity. - Giulia Preti; Matteo Riondato; Aristides Gionis; Gianmarco De Francisci Morales (WSDM, 2025)