Matteo Riondato

Contact info

Publications

Journal articles, Conference papers, Technical reports/Abstracts/Posters, Tutorials

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

Here are the BibTeX entries for my papers.

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

Journal Articles

  1. G. Preti, G. De Francisci Morales, and M. Riondato. Impossibility result for Markov chain Monte Carlo sampling from microcanonical bipartite graph ensembles. Physical Review E, 109(5):L053301, 2024.
  2. G. Preti, G. De Francisci Morales, and M. Riondato. Alice and the Caterpillar: A More Descriptive Null Model for Assessing Data Mining Results, Knowledge and Information Systems, 66(3):1917-1954, 2024.
  3. M. Abuissa, A. Lee, and M. Riondato. ROhAN: Row-order Agnostic Null Models for Statistically-sound Knowledge Discovery, Data Mining and Knowledge Discovery, 37(4):1692–1718, 2023.
  4. G. Preti, G. De Francisci Morales, and M. Riondato. MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling, ACM Transactions on Intelligent Systems and Technology, 14(3):54, 2023.
  5. C. Cousins, C. Wohlgemuth, and M. Riondato. Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages, ACM Transactions on Knowledge Discovery from Data, 17(6):78, 2023.
  6. S. Haddadan, C. Menghini, M. Riondato, and E. Upfal. Reducing Polarization and Increasing Diverse Navigability in Graph by Inserting Edges and Swapping Edge Weights, Data Mining and Knowledge Discovery (S. I. on Bias and Fairness), 36(6):2334–2378, 2022.
  7. S. Jenkins, S. Walzer-Goldfeld, and M. Riondato. SPEck: Mining Statistically-significant Sequential Patterns Efficiently with Exact Sampling, Data Mining and Knowledge Discovery, 36(4):1575–1599, 2022.
  8. L. Pellegrina, C. Cousins, F. Vandin, and M. Riondato. McRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining, ACM Transactions on Knowledge Discovery from Data, 16(6):124, 2022.
  9. 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.
  10. 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)
  11. 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)
  12. C. Cousins and M. Riondato CaDET: Interpretable Parametric Conditional Density Estimation with Decision Trees and Forests, Machine Learning, 108:1631–1634, 2019
  13. 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
  14. 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
  15. M. Riondato, D. García-Soriano, and F. Bonchi. Graph Summarization with Quality Guarantees, Data Mining and Knowledge Discovery, 31(2):314—349, 2017
  16. M. Riondato and E. M. Kornaropoulos. Fast Approximation of Betweenness Centrality through Sampling, Data Mining and Knowledge Discovery, 30(2):438—475, 2016
  17. 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
  18. 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. M. Riondato. Statistically-sound Knowledge Discovery from Data: Challenges and Directions, IEEE CogMI'23, 2023.
  2. M. Riondato. Statistically-sound Knowledge Discovery from Data, SIAM SDM'23, 2023.
  3. G. Preti, G. De Francisci Morales, and M. Riondato. Alice and the Caterpillar: A More Descriptive Null Model for Assessing Data Mining Results, IEEE ICDM'22, 2022.
  4. G. Preti, G. De Francisci Morales, and M. Riondato. MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling, ACM KDD'21, 2021.
  5. C. Cousins, C. Wohlgemuth, and M. Riondato. Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages, ACM KDD'21, 2021.
  6. S. Haddadan, C. Menghini, M. Riondato, and E. Upfal. RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions, ACM WSDM'21, 2021.
  7. C. Cousins and M. Riondato. Sharp uniform convergence bounds through empirical centralization, NeurIPS'20, 2020.
  8. 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.
  9. L. Pellegrina, M. Riondato, and F. Vandin, SPuManTE: Significant Pattern Mining with Unconditional Testing, ACM KDD'19, 2019.
  10. S. Servan-Schreiber, M. Riondato, and E. Zgraggen. ProSecCo: Progressive Sequence Mining with Convergence Guarantees, IEEE ICDM'18, 2018
  11. M. Riondato and F. Vandin. MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension, ACM KDD'18, 2018
  12. M. Riondato and E. Upfal ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages, ACM KDD'16, 2016
  13. 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
  14. A. Mahmoody, M. Riondato, and E. Upfal. Wiggins: Detecting Valuable Information in Dynamic Networks with Limited Resources, ACM WSDM'16, 2016
  15. M. Riondato and E. Upfal. Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages, ACM KDD'15, 2015, Presentation Video
  16. 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
  17. M. Riondato, D. García-Soriano, and F. Bonchi. Graph Summarization with Quality Guarantees, IEEE ICDM'14, 2014
  18. M. Riondato. Sampling-based Data Mining Algorithms: Modern Techniques and Case Studies, ECML PKDD'14, 2014
  19. M. Riondato and F. Vandin. Finding the True Frequent Itemsets, SIAM SDM'14, 2014
  20. M. Riondato and E. M. Kornaropoulos. Fast Approximation of Betweenness Centrality through Sampling, ACM WSDM'14, 2014
  21. 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
  22. M. Riondato and E. Upfal. Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees, ECML PKDD'12, 2012
  23. A. Pietracaprina, G. Pucci, M. Riondato, F. Silvestri, and E. Upfal. Space-round Tradeoffs for MapReduce Computations, ACM ICS'12, 2012
  24. M. Akdere, U. Çetintemel, M. Riondato, E. Upfal, and S. B. Zdonik. Learning-based Query Performance Modeling and Prediction, IEEE ICDE'12, 2012
  25. 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
  26. 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, Abstracts, and Posters

  1. M. Riondato. Scalable Algorithms for Hypothesis Testing. SIAM MDS'22, 2022.
  2. A. Lee, S. Walzer-Goldfeld, S. Zablah, and M. Riondato. A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract). AAAI'22, 2022 (supplement)
  3. 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. Outline, 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

Maryam Abuissa, 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, Steedman Jenkins, Evgenios M. Kornaropoulos, Alexander Lee, 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, Stefan Walzer-Goldfeld, Shukry Zablah, Stan B. Zdonik, Emanuel Zgraggen