Publications
My publications and citations on: DBLP, Google Scholar, AMiner, ResearchGate, ORCID, ResearcherID, Scopus.
Here are the BibTeX entries for my papers.
Many of the linked PDFs are extended versions of the original publications.
Journal Articles
- 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
- 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, 2017
- M. Riondato, D. García-Soriano, and F. Bonchi. Graph Summarization with Quality Guarantees, Data Mining and Knowledge Discovery, 31(2):314—349, 2017
- M. Riondato and E. M. Kornaropoulos. Fast Approximation of Betweenness Centrality through Sampling, Data Mining and Knowledge Discovery, 30(2):438—475, 2016
- 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
- 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, 2010
Conference Papers
- S. Servan-Schreiber, M. Riondato, and E. Zgraggen. ProSecCo: Progressive Sequence Mining with Convergence Guarantees, IEEE ICDM'18, 2018
- M. Riondato and F. Vandin. MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension, ACM KDD'18, 2018
- M. Riondato and E. Upfal ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages, ACM KDD'16, 2016
- 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
- A. Mahmoody, M. Riondato, and E. Upfal. Wiggins: Detecting Valuable Information in Dynamic Networks with Limited Resources, ACM WSDM'16, 2016
- M. Riondato and E. Upfal. Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages, ACM KDD'15, 2015, Presentation Video
- 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
- M. Riondato, D. García-Soriano, and F. Bonchi. Graph Summarization with Quality Guarantees, IEEE ICDM'14, 2014
- M. Riondato. Sampling-based Data Mining Algorithms: Modern Techniques and Case Studies, ECML PKDD'14, 2014
- M. Riondato and F. Vandin. Finding the True Frequent Itemsets, SIAM SDM'14, 2014
- M. Riondato and E. M. Kornaropoulos. Fast Approximation of Betweenness Centrality through Sampling, ACM WSDM'14, 2014
- 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
- M. Riondato and E. Upfal. Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees, ECML PKDD'12, 2012
- A. Pietracaprina, G. Pucci, M. Riondato, F. Silvestri, and E. Upfal. Space-round Tradeoffs for MapReduce Computations, ACM ICS'12, 2012
- M. Akdere, U. Çetintemel, M. Riondato, E. Upfal, and S. B. Zdonik. Learning-based Query Performance Modeling and Prediction, IEEE ICDE'12, 2012
- 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
- 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
- M. Riondato. Sharpe Ratio: Estimation, Confidence Intervals, and Hypothesis Testing. Two Sigma Technical Report Series, 2018-001.
Tutorials
- F. Bonchi, G. De Francisci Morales, and M. Riondato. Centrality Measures on Big Graphs: Exact, Approximated, and Distributed Algorithms, WWW'16. Slides.
- 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
- M. Riondato. Sampling-based Randomized Algorithms for Big Data Analytics, Brown University Department of Computer Science, 2014
Other technical writings
- M. Riondato, Jails, in FreeBSD Handbook.
Coauthors
Mert Akdere, Aris Anagnostopoulos, Luca Becchetti, Francesco Bonchi, Uğur Çetintemel, Justin A. DeBrabant, Gianmarco De Francisci Morales, Lorenzo De Stefani, Rodrigo Fonseca, Alessandro Epasto, David García Soriano, Evgenios M. Kornaropoulos, Ahmad Mahmoody, Ida Mele, Andrea Pietracaprina, Geppino Pucci, Sacha Servan-Schreiber, Francesco Silvestri, Eli Upfal, Fabio Vandin, Stan B. Zdonik, Emanuel Zgraggen