Matteo Riondato

Contact info

Publications

My publications and citations on: DBLP, Google Scholar, AMiner, ResearchGate, ORCID iD iconORCID, ResearcherID, Scopus.

Here are the BibTeX entries for my papers.

Many of the linked PDFs are extended versions of the original publications.

Journal articles, Conference papers, Technical report, Tutorials

Journal Articles

  1. M. A. U. Nasir, C. Aslay, G. De Francisci Morales, and M. Riondato. TipTap: Approximate Mining of Frequent 𝑘-Subgraph Patterns in Evolving Graphs, ACM Transactions on Knowledge Discovery from Data, 15(3):48, 2021.
  2. M. Riondato and F. Vandin. MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension, ACM Transactions on Knowledge Discovery from Data, 14(5):56, 2020 (S. I. for the best papers of KDD'18)
  3. S. Servan-Schreiber, M. Riondato, and E. Zgraggen. ProSecCo: Progressive Sequence Mining with Convergence Guarantees, Knowledge and Information Systems, 62(4):1313–1340 (S. I. for the best papers of ICDM'18)
  4. C. Cousins and M. Riondato CaDET: Interpretable Parametric Conditional Density Estimation with Decision Trees and Forests, Machine Learning, 108:1631–1634, 2019
  5. M. Riondato and E. Upfal ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages, ACM Transactions on Knowledge Discovery from Data, 12(5):61, 2018
  6. L. De Stefani, A. Epasto, M. Riondato, and E. Upfal. TRIÈST: Counting Local and Global Triangles in Fully-dynamic Streams with Fixed Memory Size, ACM Transactions on Knowledge Discovery from Data, 11(2):43 (S. I. for the best papers of KDD'16), 2017
  7. M. Riondato, D. García-Soriano, and F. Bonchi. Graph Summarization with Quality Guarantees, Data Mining and Knowledge Discovery, 31(2):314—349, 2017
  8. M. Riondato and E. M. Kornaropoulos. Fast Approximation of Betweenness Centrality through Sampling, Data Mining and Knowledge Discovery, 30(2):438—475, 2016
  9. M. Riondato and E. Upfal. Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees, ACM Transactions on Knowledge Discovery from Data, 8(4):20, 2014
  10. A. Pietracaprina, M. Riondato, E. Upfal, and F. Vandin. Mining Top-k Frequent Itemsets through Progressive Sampling, Data Mining and Knowledge Discovery, 21(2):310—326 (S. I. for the best papers of ECML PKDD'10), 2010

Conference Papers

  1. G. Preti, G. De Francisci Morales, and M. Riondato. MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling, ACM KDD'21, 2021.
  2. C. Cousins, C. Wohlgemuth, and M. Riondato. Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages, ACM KDD'21, 2021.
  3. S. Haddadan, C. Menghini, M. Riondato, and E. Upfal. RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions, ACM WSDM'21, 2021.
  4. C. Cousins and M. Riondato. Sharp uniform convengence bounds through empirical centralization, NeurIPS'20, 2020.
  5. L. Pellegrina, C. Cousins, F. Vandin, and M. Riondato. McRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining, ACM KDD'20, 2020.
  6. L. Pellegrina, M. Riondato, and F. Vandin, SPuManTE: Significant Pattern Mining with Unconditional Testing, ACM KDD'19, 2019.
  7. S. Servan-Schreiber, M. Riondato, and E. Zgraggen. ProSecCo: Progressive Sequence Mining with Convergence Guarantees, IEEE ICDM'18, 2018
  8. M. Riondato and F. Vandin. MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension, ACM KDD'18, 2018
  9. M. Riondato and E. Upfal ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages, ACM KDD'16, 2016
  10. L. De Stefani, A. Epasto, M. Riondato, and E. Upfal. TRIÈST: Counting Local and Global Triangles in Fully-dynamic Streams with Fixed Memory Size, ACM KDD'16, 2016
  11. A. Mahmoody, M. Riondato, and E. Upfal. Wiggins: Detecting Valuable Information in Dynamic Networks with Limited Resources, ACM WSDM'16, 2016
  12. M. Riondato and E. Upfal. Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages, ACM KDD'15, 2015, Presentation Video
  13. A. Anagnastopoulos, L. Becchetti, A. Fazzone, I. Mele, and M. Riondato. The Importance of Being Experts: Efficient Max-Finding in Crowdsourcing, ACM SIGMOD'15, 2015
  14. M. Riondato, D. García-Soriano, and F. Bonchi. Graph Summarization with Quality Guarantees, IEEE ICDM'14, 2014
  15. M. Riondato. Sampling-based Data Mining Algorithms: Modern Techniques and Case Studies, ECML PKDD'14, 2014
  16. M. Riondato and F. Vandin. Finding the True Frequent Itemsets, SIAM SDM'14, 2014
  17. M. Riondato and E. M. Kornaropoulos. Fast Approximation of Betweenness Centrality through Sampling, ACM WSDM'14, 2014
  18. M. Riondato, J. A. DeBrabant, R. Fonseca, and E. Upfal. PARMA: A Parallel Randomized Algorithm for Association Rules Mining in MapReduce, ACM CIKM'12, 2012
  19. M. Riondato and E. Upfal. Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees, ECML PKDD'12, 2012
  20. A. Pietracaprina, G. Pucci, M. Riondato, F. Silvestri, and E. Upfal. Space-round Tradeoffs for MapReduce Computations, ACM ICS'12, 2012
  21. M. Akdere, U. Çetintemel, M. Riondato, E. Upfal, and S. B. Zdonik. Learning-based Query Performance Modeling and Prediction, IEEE ICDE'12, 2012
  22. M. Riondato, M. Akdere, U. Çetintemel, S. B. Zdonik, and E. Upfal. The VC-dimension of SQL Queries and Selectivity Estimation through Sampling, ECML PKDD'11, 2011
  23. M. Akdere, U. Çetintemel, M. Riondato, E. Upfal, and S. B. Zdonik. The Case for Predictive Database Systems: Opportunities and Challenges, CIDR'11, 2011

Technical Reports

  1. M. Riondato. Sharpe Ratio: Estimation, Confidence Intervals, and Hypothesis Testing. Two Sigma Technical Report Series, 2018-001.

Tutorials

  1. L. Pellegrina, M. Riondato, and F. Vandin, Hypothesis Testing and Statistically-sound Pattern Mining (2-pager), ACM KDD'19, SIAM SDM'20 (Slides TBD).
  2. F. Bonchi, G. De Francisci Morales, and M. Riondato. Centrality Measures on Big Graphs: Exact, Approximated, and Distributed Algorithms, WWW'16. Slides.
  3. M. Riondato and E. Upfal. VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms, ACM KDD'15, ECML PKDD'15, ACM CIKM'15. Slides, Video at KDD'15 (Part 1), Video at KDD'15 (Part 2)

PhD Dissertation

  1. M. Riondato. Sampling-based Randomized Algorithms for Big Data Analytics, Brown University Department of Computer Science, 2014

Other technical writings

  1. M. Riondato, Jails, in FreeBSD Handbook.

Coauthors

Mert Akdere, Aris Anagnostopoulos, Cigdem Aslay, Luca Becchetti, Francesco Bonchi, Uğur Çetintemel, Cyrus Cousins Justin A. DeBrabant, Gianmarco De Francisci Morales, Lorenzo De Stefani, Rodrigo Fonseca, Alessandro Epasto, David García Soriano, Shahrzad Haddadan, Evgenios M. Kornaropoulos, Ahmad Mahmoody, Ida Mele, Cristina Menghini, Muhammad Anis Udin Nasir, Leonardo Pellegrina, Andrea Pietracaprina, Giulia Preti, Geppino Pucci, Sacha Servan-Schreiber, Francesco Silvestri, Eli Upfal, Fabio Vandin, Stan B. Zdonik, Emanuel Zgraggen