48 citations · 155 across the 15 of their papers we have counts for
13 papers · 1 filter
Does Compression Imply Generalization? A Minimum Description Length Perspective
Lincen Yang, Jiayang Shi, Nan Pu +4
A central question in modern learning theory is what notion of compression, if any, explains why over-parameterized neural networks generalize. Existing answers measure complexity…
Learning Subgroups with Maximum Treatment Effects without Causal Heuristics
Lincen Yang, Zhong Li, Matthijs van Leeuwen +1
Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While m…
Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
Zhong Li, Qi Huang, Yuxuan Zhu +4
We introduce Time-Conditioned Contraction Matching (TCCM), a novel method for semi-supervised anomaly detection in tabular data. TCCM is inspired by flow matching, a recent generat…
Diffusion and Flow Matching Models for Tabular Data: A Survey
Zhong Li, Qi Huang, Lincen Yang +5
Deep generative models have made rapid progress in image, text, audio, and video generation, and are increasingly being applied to structured records. For tabular data, however, ge…
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection
Zhong Li, Yuhang Wang, Matthijs van Leeuwen
Self-supervised learning (SSL) is an emerging paradigm that exploits supervisory signals generated from the data itself, and many recent studies have leveraged SSL to conduct graph…
Explainable Graph Neural Networks Under Fire
Zhong Li, Simon Geisler, Yuhang Wang +2
Predictions made by graph neural networks (GNNs) usually lack interpretability due to their complex computational behavior and the abstract nature of graphs. In an attempt to tackl…
Explainable Contextual Anomaly Detection using Quantile Regression Forests
Zhong Li, Matthijs van Leeuwen
Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection metho…
A Survey on Explainable Anomaly Detection
Zhong Li, Yuxuan Zhu, Matthijs van Leeuwen
In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding m…
Feature Selection for Fault Detection and Prediction based on Event Log Analysis
Zhong Li, Matthijs van Leeuwen
Event logs are widely used for anomaly detection and prediction in complex systems. Existing log-based anomaly detection methods usually consist of four main steps: log collection,…
Robust subgroup discovery
Hugo Manuel Proença, Peter Grünwald, Thomas Bäck +1
We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes,…
Discovering outstanding subgroup lists for numeric targets using MDL
Hugo M. Proença, Peter Grünwald, Thomas Bäck +1
The task of subgroup discovery (SD) is to find interpretable descriptions of subsets of a dataset that stand out with respect to a target attribute. To address the problem of minin…
Vouw: Geometric Pattern Mining using the MDL Principle
Micky Faas, Matthijs van Leeuwen
We introduce geometric pattern mining, the problem of finding recurring local structure in discrete, geometric matrices. It differs from existing pattern mining problems by identif…
Interpretable multiclass classification by MDL-based rule lists
Hugo M. Proença, Matthijs van Leeuwen
Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their mo…