Matteo Riondato

Knowl. Inf. Syst., March, 2024

2023

Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher Averages.

[BibT_eX]

[DOI]

Cyrus Cousins

Chloe Wohlgemuth

ACM Trans. Knowl. Discov. Data, July, 2023

MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling.

[BibT_eX]

[DOI]

Giulia Preti

ACM Trans. Intell. Syst. Technol., 2023

ROhAN: Row-order agnostic null models for statistically-sound knowledge discovery.

[BibT_eX]

[DOI]

Maryam Abuissa

Alexander W. Lee

Data Min. Knowl. Discov., 2023

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

[BibT_eX]

[DOI]

Giulia Preti

CoRR, 2023

Statistically-sound Knowledge Discovery from Data.

[BibT_eX]

[DOI]

Proceedings of the 2023 SIAM International Conference on Data Mining, 2023

Statistically-Sound Knowledge Discovery from Data: Challenges and Directions.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

ACM Trans. Knowl. Discov. Data, 2022

SPEck: mining statistically-significant sequential patterns efficiently with exact sampling.

[BibT_eX]

[DOI]

Steedman Jenkins

Stefan Walzer-Goldfeld

Data Min. Knowl. Discov., 2022

Reducing polarization and increasing diverse navigability in graphs by inserting edges and swapping edge weights.

[BibT_eX]

[DOI]

Data Min. Knowl. Discov., 2022

A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract).

[BibT_eX]

[DOI]

Alexander W. Lee

Stefan Walzer-Goldfeld

Shukry Zablah

Muhammad Anis Uddin Nasir

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.

[BibT_eX]

[DOI]

Çigdem Aslay

ACM Trans. Knowl. Discov. Data, 2021

RePBubLik: Reducing Polarized Bubble Radius with Link Insertions.

[BibT_eX]

[DOI]

Proceedings of the WSDM '21, 2021

2020

MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension.

[BibT_eX]

[DOI]

ACM Trans. Knowl. Discov. Data, 2020

ProSecCo: progressive sequence mining with convergence guarantees.

[BibT_eX]

[DOI]

Sacha Servan-Schreiber

Emanuel Zgraggen

Knowl. Inf. Syst., 2020

Sharp uniform convergence bounds through empirical centralization.

[BibT_eX]

[DOI]

Cyrus Cousins

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.

[BibT_eX]

[DOI]

Cyrus Cousins

Mach. Learn., 2019

Hypothesis Testing and Statistically-sound Pattern Mining.

[BibT_eX]

[DOI]

Leonardo Pellegrina

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

SPuManTE: Significant Pattern Mining with Unconditional Testing.

[BibT_eX]

[DOI]

Leonardo Pellegrina

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.

[BibT_eX]

[DOI]

ACM Trans. Knowl. Discov. Data, 2018

2017

TRIÈST: Counting Local and Global Triangles in Fully Dynamic Streams with Fixed Memory Size.

[BibT_eX]

[DOI]

ACM Trans. Knowl. Discov. Data, 2017

Graph summarization with quality guarantees.

[BibT_eX]

[DOI]

David García-Soriano

Francesco Bonchi

Data Min. Knowl. Discov., 2017

2016

Fast approximation of betweenness centrality through sampling.

[BibT_eX]

[DOI]

Evgenios M. Kornaropoulos

Data Min. Knowl. Discov., 2016

Centrality Measures on Big Graphs: Exact, Approximated, and Distributed Algorithms.

[BibT_eX]

[DOI]

Francesco Bonchi

Proceedings of the 25th International Conference on World Wide Web, 2016

Wiggins: Detecting Valuable Information in Dynamic Networks Using Limited Resources.

[BibT_eX]

[DOI]

Ahmad Mahmoody

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages.

[BibT_eX]

[DOI]

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2014

Sampling-based Randomized Algorithms for Big Data Analytics.

[BibT_eX]

[DOI]

PhD thesis, 2014

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees.

[BibT_eX]

[DOI]

ACM Trans. Knowl. Discov. Data, 2014

Finding the True Frequent Itemsets.

[BibT_eX]

[DOI]

Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

Sampling-Based Data Mining Algorithms: Modern Techniques and Case Studies.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

2013

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

[BibT_eX]

[DOI]