Matteo Riondato

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 40

Top Topics

Top Venues

ACM Trans. Knowl. Discov. Data

Data Min. Knowl. Discov.

Publications

Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato
Alice and the Caterpillar: A more descriptive null model for assessing data mining results. Knowl. Inf. Syst. 66 (3) (2024)
Cyrus Cousins, Chloe Wohlgemuth, Matteo Riondato
Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher Averages. ACM Trans. Knowl. Discov. Data 17 (6) (2023)
Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato
An impossibility result for Markov Chain Monte Carlo sampling from micro-canonical bipartite graph ensembles. CoRR (2023)
Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato
MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling. ACM Trans. Intell. Syst. Technol. 14 (3) (2023)
Matteo Riondato
Statistically-sound Knowledge Discovery from Data. SDM (2023)
Matteo Riondato
Statistically-Sound Knowledge Discovery from Data: Challenges and Directions. CogMI (2023)
Maryam Abuissa, Alexander W. Lee, Matteo Riondato
ROhAN: Row-order agnostic null models for statistically-sound knowledge discovery. Data Min. Knowl. Discov. 37 (4) (2023)
Alexander Lee, Stefan Walzer-Goldfeld, Shukry Zablah, Matteo Riondato
A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract). AAAI (2022)
Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining. ACM Trans. Knowl. Discov. Data 16 (6) (2022)
Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato
Alice and the Caterpillar: A More Descriptive Null Model for Assessing Data Mining Results. ICDM (2022)
Steedman Jenkins, Stefan Walzer-Goldfeld, Matteo Riondato
SPEck: mining statistically-significant sequential patterns efficiently with exact sampling. Data Min. Knowl. Discov. 36 (4) (2022)
Shahrzad Haddadan, Cristina Menghini, Matteo Riondato, Eli Upfal
Reducing polarization and increasing diverse navigability in graphs by inserting edges and swapping edge weights. Data Min. Knowl. Discov. 36 (6) (2022)
Shahrzad Haddadan, Cristina Menghini, Matteo Riondato, Eli Upfal
RePBubLik: Reducing Polarized Bubble Radius with Link Insertions. WSDM (2021)
Muhammad Anis Uddin Nasir, Çigdem Aslay, Gianmarco De Francisci Morales, Matteo Riondato
-Subgraph Patterns in Evolving Graphs. ACM Trans. Knowl. Discov. Data 15 (3) (2021)
Cyrus Cousins, Chloe Wohlgemuth, Matteo Riondato
Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages. KDD (2021)
Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato
MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling. KDD (2021)
Shahrzad Haddadan, Cristina Menghini, Matteo Riondato, Eli Upfal
RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions. CoRR (2021)
Cyrus Cousins, Matteo Riondato
Sharp uniform convergence bounds through empirical centralization. NeurIPS (2020)
Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining. CoRR (2020)
Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining. KDD (2020)
Matteo Riondato, Fabio Vandin
MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension. ACM Trans. Knowl. Discov. Data 14 (5) (2020)
Sacha Servan-Schreiber, Matteo Riondato, Emanuel Zgraggen
ProSecCo: progressive sequence mining with convergence guarantees. Knowl. Inf. Syst. 62 (4) (2020)
Leonardo Pellegrina, Matteo Riondato, Fabio Vandin
Hypothesis Testing and Statistically-sound Pattern Mining. KDD (2019)
Cyrus Cousins, Matteo Riondato
CaDET: interpretable parametric conditional density estimation with decision trees and forests. Mach. Learn. 108 (8-9) (2019)
Leonardo Pellegrina, Matteo Riondato, Fabio Vandin
SPuManTE: Significant Pattern Mining with Unconditional Testing. KDD (2019)
Matteo Riondato, Eli Upfal
ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages. ACM Trans. Knowl. Discov. Data 12 (5) (2018)
Sacha Servan-Schreiber, Matteo Riondato, Emanuel Zgraggen
ProSecCo: Progressive Sequence Mining with Convergence Guarantees. ICDM (2018)
Matteo Riondato, Fabio Vandin
MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension. KDD (2018)
Lorenzo De Stefani, Alessandro Epasto, Matteo Riondato, Eli Upfal
TRIÈST: Counting Local and Global Triangles in Fully Dynamic Streams with Fixed Memory Size. ACM Trans. Knowl. Discov. Data 11 (4) (2017)
Matteo Riondato, David García-Soriano, Francesco Bonchi
Graph summarization with quality guarantees. Data Min. Knowl. Discov. 31 (2) (2017)
Lorenzo De Stefani, Alessandro Epasto, Matteo Riondato, Eli Upfal
TRIÈST: Counting Local and Global Triangles in Fully-dynamic Streams with Fixed Memory Size. CoRR (2016)
Lorenzo De Stefani, Alessandro Epasto, Matteo Riondato, Eli Upfal
TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size. KDD (2016)
Matteo Riondato, Eli Upfal
ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages. KDD (2016)
Matteo Riondato, Evgenios M. Kornaropoulos
Fast approximation of betweenness centrality through sampling. Data Min. Knowl. Discov. 30 (2) (2016)
Francesco Bonchi, Gianmarco De Francisci Morales, Matteo Riondato
Centrality Measures on Big Graphs: Exact, Approximated, and Distributed Algorithms. WWW (Companion Volume) (2016)
Ahmad Mahmoody, Matteo Riondato, Eli Upfal
Wiggins: Detecting Valuable Information in Dynamic Networks Using Limited Resources. WSDM (2016)
Matteo Riondato, Eli Upfal
ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages. CoRR (2016)
Matteo Riondato, Eli Upfal
VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms. KDD (2015)
Aris Anagnostopoulos, Luca Becchetti, Adriano Fazzone, Ida Mele, Matteo Riondato
The Importance of Being Expert: Efficient Max-Finding in Crowdsourcing. SIGMOD Conference (2015)
Matteo Riondato, Eli Upfal
Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages. KDD (2015)
Matteo Riondato, David García-Soriano, Francesco Bonchi
Graph Summarization with Quality Guarantees. ICDM (2014)
Matteo Riondato, Fabio Vandin
Finding the True Frequent Itemsets. SDM (2014)
Matteo Riondato, Eli Upfal
Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees. ACM Trans. Knowl. Discov. Data 8 (4) (2014)
Matteo Riondato, Evgenios M. Kornaropoulos
Fast approximation of betweenness centrality through sampling. WSDM (2014)
Matteo Riondato
Sampling-Based Data Mining Algorithms: Modern Techniques and Case Studies. ECML/PKDD (3) (2014)
Matteo Riondato, Fabio Vandin
Controlling False Positives in Frequent Itemsets Mining through the VC-Dimension CoRR (2013)
Matteo Riondato, Justin A. DeBrabant, Rodrigo Fonseca, Eli Upfal
PARMA: a parallel randomized algorithm for approximate association rules mining in MapReduce. CIKM (2012)
Andrea Pietracaprina, Geppino Pucci, Matteo Riondato, Francesco Silvestri, Eli Upfal
Space-round tradeoffs for MapReduce computations. ICS (2012)
Mert Akdere, Ugur Çetintemel, Matteo Riondato, Eli Upfal, Stanley B. Zdonik
Learning-based Query Performance Modeling and Prediction. ICDE (2012)
Matteo Riondato, Eli Upfal
Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees. ECML/PKDD (1) (2012)
Matteo Riondato, Eli Upfal
Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees CoRR (2011)
Mert Akdere, Ugur Çetintemel, Matteo Riondato, Eli Upfal, Stanley B. Zdonik
The Case for Predictive Database Systems: Opportunities and Challenges. CIDR (2011)
Matteo Riondato, Mert Akdere, Ugur Çetintemel, Stanley B. Zdonik, Eli Upfal
The VC-Dimension of Queries and Selectivity Estimation Through Sampling CoRR (2011)
Andrea Pietracaprina, Geppino Pucci, Matteo Riondato, Francesco Silvestri, Eli Upfal
Space-Round Tradeoffs for MapReduce Computations CoRR (2011)
Matteo Riondato, Mert Akdere, Ugur Çetintemel, Stanley B. Zdonik, Eli Upfal
The VC-Dimension of SQL Queries and Selectivity Estimation through Sampling. ECML/PKDD (2) (2011)
Andrea Pietracaprina, Matteo Riondato, Eli Upfal, Fabio Vandin
frequent itemsets through progressive sampling. Data Min. Knowl. Discov. 21 (2) (2010)
Andrea Pietracaprina, Matteo Riondato, Eli Upfal, Fabio Vandin
Mining Top-K Frequent Itemsets Through Progressive Sampling CoRR (2010)