Wouter Duivesteijn

About Wouter Duivesteijn

Wouter Duivesteijn, With an exceptional h-index of 17 and a recent h-index of 15 (since 2020), a distinguished researcher at Technische Universiteit Eindhoven,

His recent articles reflect a diverse array of research interests and contributions to the field:

RMI-RRG: A Soft Protocol to Postulate Monotonicity Constraints for Tabular Datasets

Introducing exceptional growth mining—Analyzing the impact of soil characteristics on on-farm crop growth and yield variability

Bottom-Up Search: A Distance-Based Search Strategy for Supervised Local Pattern Mining on Multi-Dimensional Target Spaces

Exceptional In So Many Domains Exploring Exceptional Model Mining in Unstructured Data

A Clustering-Inspired Quality Measure for Exceptional Preferences Mining—Design Choices and Consequences

Islands of Confidence: Robust Neural Network Classification with Uncertainty Quantification

Efficient Subgroup Discovery Through Auto-Encoding

Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS)

Wouter Duivesteijn Information

University

Position

___

Citations(all)

1026

Citations(since 2020)

628

Cited By

704

hIndex(all)

17

hIndex(since 2020)

15

i10Index(all)

22

i10Index(since 2020)

17

Email

University Profile Page

Google Scholar

Top articles of Wouter Duivesteijn

RMI-RRG: A Soft Protocol to Postulate Monotonicity Constraints for Tabular Datasets

2024/4/16

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

Introducing exceptional growth mining—Analyzing the impact of soil characteristics on on-farm crop growth and yield variability

Plos one

2024/1/29

Pytrik Reidsma
Pytrik Reidsma

H-Index: 30

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

Bottom-Up Search: A Distance-Based Search Strategy for Supervised Local Pattern Mining on Multi-Dimensional Target Spaces

age

2023/7

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

Exceptional In So Many Domains Exploring Exceptional Model Mining in Unstructured Data

2023

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

A Clustering-Inspired Quality Measure for Exceptional Preferences Mining—Design Choices and Consequences

2022/10/10

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

Rianne Margaretha Schouten
Rianne Margaretha Schouten

H-Index: 3

Islands of Confidence: Robust Neural Network Classification with Uncertainty Quantification

2022/9/29

Sibylle Hess
Sibylle Hess

H-Index: 5

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

Efficient Subgroup Discovery Through Auto-Encoding

2022/4/7

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS)

2022

Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functions

Data Mining and Knowledge Discovery

2022/1/1

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

Mykola Pechenizkiy
Mykola Pechenizkiy

H-Index: 31

Beyond discriminant patterns: On the robustness of decision rule ensembles

arXiv preprint arXiv:2109.10432

2021/9/21

Exceptional gestalt mining: combining magic cards to make complex coalitions thrive

2021/9/13

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

Adversarial balancing-based representation learning for causal effect inference with observational data

Data Mining and Knowledge Discovery

2021/7

Exceptional in so many ways—discovering descriptors that display exceptional behavior on contrasting scenarios

IEEE Access

2020/10/30

Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling

Data Mining and Knowledge Discovery

2020/9

How to cheat the page limit

2020/5

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

Sibylle Hess
Sibylle Hess

H-Index: 5

Xin Du
Xin Du

H-Index: 5

Predicting Remaining Useful Life with Similarity-Based Priors

2020

Remco Dijkman
Remco Dijkman

H-Index: 28

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

Fairness in network representation by latent structural heterogeneity in observational data

2020

Softmax-based classification is k-means clustering: Formal proof, consequences for adversarial attacks, and improvement through centroid based tailoring

arXiv preprint arXiv:2001.01987

2020/1/7

Sibylle Hess
Sibylle Hess

H-Index: 5

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

DEvIANT: Discovering significant exceptional (dis-) agreement within groups

2020

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

k Is the Magic Number—Inferring the Number of Clusters Through Nonparametric Concentration Inequalities

2020

Sibylle Hess
Sibylle Hess

H-Index: 5

Wouter Duivesteijn
Wouter Duivesteijn

H-Index: 13

See List of Professors in Wouter Duivesteijn University(Technische Universiteit Eindhoven)