Matteo Riondato

Orcid: 0000-0003-2523-4420

Affiliations:
  • Amherst College, MA, USA


According to our database1, Matteo Riondato authored at least 43 papers between 2010 and 2024.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Alice and the Caterpillar: A more descriptive null model for assessing data mining results.
Knowl. Inf. Syst., March, 2024

Polaris: Sampling from the Multigraph Configuration Model with Prescribed Color Assortativity.
CoRR, 2024

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

2023
Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher Averages.
ACM Trans. Knowl. Discov. Data, July, 2023

MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling.
ACM Trans. Intell. Syst. Technol., 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
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining.
ACM Trans. Knowl. Discov. Data, 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

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

2020
MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension.
ACM Trans. Knowl. Discov. Data, 2020

ProSecCo: progressive sequence mining with convergence guarantees.
Knowl. Inf. Syst., 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

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
ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages.
ACM Trans. Knowl. Discov. Data, 2018

2017
TRIÈST: Counting Local and Global Triangles in Fully Dynamic Streams with Fixed Memory Size.
ACM Trans. Knowl. Discov. Data, 2017

Graph summarization with quality guarantees.
Data Min. Knowl. Discov., 2017

2016
Fast approximation of betweenness centrality through sampling.
Data Min. Knowl. Discov., 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

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

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees.
ACM Trans. Knowl. Discov. Data, 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

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

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


  Loading...