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ORCIDMatteo Riondato
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- affiliation: Amherst College, MA, USA
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2020 – today
- 2024
[j17]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): 1917-1954 (2024)- [c31]Daniel Flores García, Hugo Flores García, Matteo Riondato:
ClaveNet: Generating Afro-Cuban Drum Patterns through Data Augmentation. Audio Mostly Conference 2024: 355-361 - [e2]Shashi Shekhar, Vagelis Papalexakis, Jing Gao, Zhe Jiang, Matteo Riondato:
Proceedings of the 2024 SIAM International Conference on Data Mining, SDM 2024, Houston, TX, USA, April 18-20, 2024. SIAM 2024, ISBN 978-1-61197-803-2 [contents] - [i11]Giulia Preti, Matteo Riondato, Aristides Gionis, Gianmarco De Francisci Morales:
Polaris: Sampling from the Multigraph Configuration Model with Prescribed Color Assortativity. CoRR abs/2409.01363 (2024) - 2023
- [j16]Maryam Abuissa, Alexander W. Lee, Matteo Riondato:
ROhAN: Row-order agnostic null models for statistically-sound knowledge discovery. Data Min. Knowl. Discov. 37(4): 1692-1718 (2023) - [j15]Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato:
MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling. ACM Trans. Intell. Syst. Technol. 14(3): 54:1-54:29 (2023) - [j14]Cyrus Cousins, Chloe Wohlgemuth, Matteo Riondato:
Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher Averages. ACM Trans. Knowl. Discov. Data 17(6): 78:1-78:47 (2023) - [c30]Matteo Riondato:
Statistically-Sound Knowledge Discovery from Data: Challenges and Directions. CogMI 2023: 97-102 - [c29]Matteo Riondato:
Statistically-sound Knowledge Discovery from Data. SDM 2023: 949-952 - [i10]Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato:
An impossibility result for Markov Chain Monte Carlo sampling from micro-canonical bipartite graph ensembles. CoRR abs/2308.10838 (2023) - 2022
- [j13]Steedman Jenkins, Stefan Walzer-Goldfeld, Matteo Riondato:
SPEck: mining statistically-significant sequential patterns efficiently with exact sampling. Data Min. Knowl. Discov. 36(4): 1575-1599 (2022) - [j12]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): 2334-2378 (2022) - [j11]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): 124:1-124:29 (2022) - [c28]Alexander W. Lee, Stefan Walzer-Goldfeld, Shukry Zablah, Matteo Riondato:
A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract). AAAI 2022: 12991-12992 - [c27]Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato:
Alice and the Caterpillar: A More Descriptive Null Model for Assessing Data Mining Results. ICDM 2022: 418-427 - [e1]Arindam Banerjee, Zhi-Hua Zhou, Evangelos E. Papalexakis, Matteo Riondato:
Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022, Alexandria, VA, USA, April 28-30, 2022. SIAM 2022, ISBN 978-1-61197-717-2 [contents] - 2021
- [j10]Muhammad Anis Uddin Nasir, Çigdem Aslay, Gianmarco De Francisci Morales, Matteo Riondato:
TipTap: Approximate Mining of Frequent k-Subgraph Patterns in Evolving Graphs. ACM Trans. Knowl. Discov. Data 15(3): 48:1-48:35 (2021) - [c26]Cyrus Cousins, Chloe Wohlgemuth, Matteo Riondato:
Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages. KDD 2021: 196-206 - [c25]Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato:
MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling. KDD 2021: 1348-1358 - [c24]Shahrzad Haddadan, Cristina Menghini, Matteo Riondato, Eli Upfal:
RePBubLik: Reducing Polarized Bubble Radius with Link Insertions. WSDM 2021: 139-147 - [i9]Shahrzad Haddadan, Cristina Menghini, Matteo Riondato, Eli Upfal:
RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions. CoRR abs/2101.04751 (2021) - 2020
- [j9]Sacha Servan-Schreiber, Matteo Riondato, Emanuel Zgraggen:
ProSecCo: progressive sequence mining with convergence guarantees. Knowl. Inf. Syst. 62(4): 1313-1340 (2020) - [j8]Matteo Riondato, Fabio Vandin:
MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension. ACM Trans. Knowl. Discov. Data 14(5): 56:1-56:31 (2020) - [c23]Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato:
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining. KDD 2020: 2165-2174 - [c22]Cyrus Cousins, Matteo Riondato:
Sharp uniform convergence bounds through empirical centralization. NeurIPS 2020 - [i8]Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato:
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining. CoRR abs/2006.09085 (2020)
2010 – 2019
- 2019
- [j7]Cyrus Cousins, Matteo Riondato:
CaDET: interpretable parametric conditional density estimation with decision trees and forests. Mach. Learn. 108(8-9): 1613-1634 (2019) - [c21]Leonardo Pellegrina, Matteo Riondato, Fabio Vandin:
SPuManTE: Significant Pattern Mining with Unconditional Testing. KDD 2019: 1528-1538 - [c20]Leonardo Pellegrina, Matteo Riondato, Fabio Vandin:
Hypothesis Testing and Statistically-sound Pattern Mining. KDD 2019: 3215-3216 - 2018
- [j6]Matteo Riondato, Eli Upfal:
ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages. ACM Trans. Knowl. Discov. Data 12(5): 61:1-61:38 (2018) - [c19]Sacha Servan-Schreiber, Matteo Riondato, Emanuel Zgraggen:
ProSecCo: Progressive Sequence Mining with Convergence Guarantees. ICDM 2018: 417-426 - [c18]Matteo Riondato, Fabio Vandin:
MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension. KDD 2018: 2130-2139 - 2017
- [j5]Matteo Riondato, David García-Soriano, Francesco Bonchi:
Graph summarization with quality guarantees. Data Min. Knowl. Discov. 31(2): 314-349 (2017) - [j4]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): 43:1-43:50 (2017) - 2016
- [j3]Matteo Riondato, Evgenios M. Kornaropoulos:
Fast approximation of betweenness centrality through sampling. Data Min. Knowl. Discov. 30(2): 438-475 (2016) - [c17]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: 825-834 - [c16]Matteo Riondato, Eli Upfal:
ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages. KDD 2016: 1145-1154 - [c15]Ahmad Mahmoody, Matteo Riondato, Eli Upfal:
Wiggins: Detecting Valuable Information in Dynamic Networks Using Limited Resources. WSDM 2016: 677-686 - [c14]Francesco Bonchi, Gianmarco De Francisci Morales, Matteo Riondato:
Centrality Measures on Big Graphs: Exact, Approximated, and Distributed Algorithms. WWW (Companion Volume) 2016: 1017-1020 - [i7]Matteo Riondato, Eli Upfal:
ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages. CoRR abs/1602.05866 (2016) - [i6]Lorenzo De Stefani, Alessandro Epasto, Matteo Riondato, Eli Upfal:
TRIÈST: Counting Local and Global Triangles in Fully-dynamic Streams with Fixed Memory Size. CoRR abs/1602.07424 (2016) - 2015
- [c13]Matteo Riondato, Eli Upfal:
Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages. KDD 2015: 1005-1014 - [c12]Matteo Riondato, Eli Upfal:
VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms. KDD 2015: 2321-2322 - [c11]Aris Anagnostopoulos, Luca Becchetti, Adriano Fazzone, Ida Mele, Matteo Riondato:
The Importance of Being Expert: Efficient Max-Finding in Crowdsourcing. SIGMOD Conference 2015: 983-998 - 2014
- [b1]Matteo Riondato:
Sampling-based Randomized Algorithms for Big Data Analytics. Brown University, USA, 2014 - [j2]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): 20:1-20:32 (2014) - [c10]Matteo Riondato, David García-Soriano, Francesco Bonchi:
Graph Summarization with Quality Guarantees. ICDM 2014: 947-952 - [c9]Matteo Riondato:
Sampling-Based Data Mining Algorithms: Modern Techniques and Case Studies. ECML/PKDD (3) 2014: 516-519 - [c8]Matteo Riondato, Fabio Vandin:
Finding the True Frequent Itemsets. SDM 2014: 497-505 - [c7]Matteo Riondato, Evgenios M. Kornaropoulos:
Fast approximation of betweenness centrality through sampling. WSDM 2014: 413-422 - 2013
- [i5]Matteo Riondato, Fabio Vandin:
Controlling False Positives in Frequent Itemsets Mining through the VC-Dimension. CoRR abs/1301.1218 (2013) - 2012
- [c6]Matteo Riondato, Justin A. DeBrabant, Rodrigo Fonseca, Eli Upfal:
PARMA: a parallel randomized algorithm for approximate association rules mining in MapReduce. CIKM 2012: 85-94 - [c5]Mert Akdere, Ugur Çetintemel, Matteo Riondato, Eli Upfal, Stanley B. Zdonik:
Learning-based Query Performance Modeling and Prediction. ICDE 2012: 390-401 - [c4]Andrea Pietracaprina, Geppino Pucci, Matteo Riondato, Francesco Silvestri, Eli Upfal:
Space-round tradeoffs for MapReduce computations. ICS 2012: 235-244 - [c3]Matteo Riondato, Eli Upfal:
Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees. ECML/PKDD (1) 2012: 25-41 - 2011
- [c2]Mert Akdere, Ugur Çetintemel, Matteo Riondato, Eli Upfal, Stanley B. Zdonik:
The Case for Predictive Database Systems: Opportunities and Challenges. CIDR 2011: 167-174 - [c1]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: 661-676 - [i4]Matteo Riondato, Mert Akdere, Ugur Çetintemel, Stanley B. Zdonik, Eli Upfal:
The VC-Dimension of Queries and Selectivity Estimation Through Sampling. CoRR abs/1101.5805 (2011) - [i3]Andrea Pietracaprina, Geppino Pucci, Matteo Riondato, Francesco Silvestri, Eli Upfal:
Space-Round Tradeoffs for MapReduce Computations. CoRR abs/1111.2228 (2011) - [i2]Matteo Riondato, Eli Upfal:
Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees. CoRR abs/1111.6937 (2011) - 2010
- [j1]Andrea Pietracaprina, Matteo Riondato, Eli Upfal, Fabio Vandin:
Mining top-K frequent itemsets through progressive sampling. Data Min. Knowl. Discov. 21(2): 310-326 (2010) - [i1]Andrea Pietracaprina, Matteo Riondato, Eli Upfal, Fabio Vandin:
Mining Top-K Frequent Itemsets Through Progressive Sampling. CoRR abs/1006.5235 (2010)
Coauthor Index
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