Matteo Riondato

Orcid: 0000-0003-2523-4420

Affiliations:
  • Amherst College, MA, USA


According to our database1, Matteo Riondato authored at least 45 papers between 2010 and 2025.

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Bibliography

2025
Polaris: Sampling from the Multigraph Configuration Model with Prescribed Color Assortativity.
Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, 2025

DiNgHy: Null Models for Non-degenerate Directed Hypergraphs.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2025

2024
Statistical and Probabilistic Methods in Algorithmic Data Analysis (Dagstuhl Seminar 24391).
Dagstuhl Reports, 2024

ClaveNet: Generating Afro-Cuban Drum Patterns through Data Augmentation.
Proceedings of the 19th International Audio Mostly Conference: Explorations in Sonic Cultures, 2024

2023
ROhAN: Row-order agnostic null models for statistically-sound knowledge discovery.
Data Min. Knowl. Discov., 2023

An impossibility result for Markov Chain Monte Carlo sampling from micro-canonical bipartite graph ensembles.
CoRR, 2023

Statistically-sound Knowledge Discovery from Data.
Proceedings of the 2023 SIAM International Conference on Data Mining, 2023

Statistically-Sound Knowledge Discovery from Data: Challenges and Directions.
Proceedings of the 5th IEEE International Conference on Cognitive Machine Intelligence, 2023

2022
SPEck: mining statistically-significant sequential patterns efficiently with exact sampling.
Data Min. Knowl. Discov., 2022

Reducing polarization and increasing diverse navigability in graphs by inserting edges and swapping edge weights.
Data Min. Knowl. Discov., 2022

Alice and the Caterpillar: A More Descriptive Null Model for Assessing Data Mining Results.
Proceedings of the IEEE International Conference on Data Mining, 2022

A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract).
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
TipTap: Approximate Mining of Frequent <i>k</i>-Subgraph Patterns in Evolving Graphs.
ACM Trans. Knowl. Discov. Data, 2021

RePBubLik: Reducing Polarized Bubble Radius with Link Insertions.
Proceedings of the WSDM '21, 2021

MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2020
Sharp uniform convergence bounds through empirical centralization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2019
CaDET: interpretable parametric conditional density estimation with decision trees and forests.
Mach. Learn., 2019

Hypothesis Testing and Statistically-sound Pattern Mining.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

SPuManTE: Significant Pattern Mining with Unconditional Testing.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

2018
MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

ProSecCo: Progressive Sequence Mining with Convergence Guarantees.
Proceedings of the IEEE International Conference on Data Mining, 2018

2016
Centrality Measures on Big Graphs: Exact, Approximated, and Distributed Algorithms.
Proceedings of the 25th International Conference on World Wide Web, 2016

Wiggins: Detecting Valuable Information in Dynamic Networks Using Limited Resources.
Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, 2016

TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015
The Importance of Being Expert: Efficient Max-Finding in Crowdsourcing.
Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, May 31, 2015

VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2014
Sampling-based Randomized Algorithms for Big Data Analytics.
PhD thesis, 2014

Fast approximation of betweenness centrality through sampling.
Proceedings of the Seventh ACM International Conference on Web Search and Data Mining, 2014

Finding the True Frequent Itemsets.
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

Sampling-Based Data Mining Algorithms: Modern Techniques and Case Studies.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Graph Summarization with Quality Guarantees.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

2013
Controlling False Positives in Frequent Itemsets Mining through the VC-Dimension
CoRR, 2013

2012
Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Space-round tradeoffs for MapReduce computations.
Proceedings of the International Conference on Supercomputing, 2012

Learning-based Query Performance Modeling and Prediction.
Proceedings of the IEEE 28th International Conference on Data Engineering (ICDE 2012), 2012

PARMA: a parallel randomized algorithm for approximate association rules mining in MapReduce.
Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012

2011
The VC-Dimension of Queries and Selectivity Estimation Through Sampling
CoRR, 2011

The VC-Dimension of SQL Queries and Selectivity Estimation through Sampling.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

The Case for Predictive Database Systems: Opportunities and Challenges.
Proceedings of the Fifth Biennial Conference on Innovative Data Systems Research, 2011

2010
Mining top-<i>K</i> frequent itemsets through progressive sampling.
Data Min. Knowl. Discov., 2010

Mining Top-K Frequent Itemsets Through Progressive Sampling
CoRR, 2010


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