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What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rules
Tim Nico Bauerschmidt, Isabel Valera, Jilles Vreeken
In real-world applications, being able to explain why a prediction is made is often as important - if not more so - than achieving high accuracy. However, existing methods for sequ…
Mapping and Advancing the Scalability-Accuracy Frontier of Nonlinear Causal Discovery
Hendrik Suhr, Sascha Xu, Jilles Vreeken
Scalable nonlinear causal discovery requires methods that combine flexible mechanism estimators with efficient search over large graph spaces. Several algorithmic families have bee…
In-Context Time Series Classification with Random Convolutional Features
Joscha Cüppers, Jilles Vreeken
Time series classification is central to domains such as medical signal analysis, industrial monitoring, and sensor-based activity recognition, where class information manifests as…
Identifying Structural Biases from Causal Mechanism Shifts
Praharsh Nanavati, Jilles Vreeken, David Kaltenpoth
Causal discovery methods commonly assume that all data is independently and identically distributed (i.i.d.) and that there are no unmeasured variables affecting the system. In pra…
Learning Causal Orderings for In-Context Tabular Prediction
Sascha Xu, Sarah Mameche, Jilles Vreeken
In-context learning for tabular data sets strong predictive standards in observational settings; it however primarily relies on correlational structure, which becomes unreliable un…
Differential Subgroup Discovery: Characterizing Where Two Populations Differ, and Why
Sascha Xu, Jilles Vreeken
We study the problem of understanding where two populations differ within a feature space, which we formalize in the concept of a differential subgroup: a subset of individuals fro…
Discovering Subgroups with Exceptional Survival Characteristics
Mhd Jawad Al Rahwanji, Sascha Xu, Nils Philipp Walter +1
In many applications, it is important to identify subpopulations that survive longer or shorter than the rest of the population. In medicine, for example, it allows determining whi…
Root Cause Analysis of Measurement and Mechanistic Anomalies
Hendrik Suhr, David Kaltenpoth, Jilles Vreeken
Root cause analysis of anomalies aims to identify how and why a sample deviates from the normal process. Existing methods primarily focus on telling which features are responsible,…
When Flatness Does (Not) Guarantee Adversarial Robustness
Nils Philipp Walter, Linara Adilova, Jilles Vreeken +1
Despite their empirical success, neural networks remain vulnerable to small, adversarial perturbations. A longstanding hypothesis suggests that flat minima, regions of low curvatur…
SpaceTime: Causal Discovery from Non-Stationary Time Series
Sarah Mameche, Lénaïg Cornanguer, Urmi Ninad +1
Understanding causality is challenging and often complicated by changing causal relationships over time and across environments. Climate patterns, for example, shift over time with…
Neuro-Symbolic Rule Lists
Sascha Xu, Nils Philipp Walter, Jilles Vreeken
Machine learning models deployed in sensitive areas such as healthcare must be interpretable to ensure accountability and fairness. Rule lists (if Age < 35 Priors > 0 then…
Federated Binary Matrix Factorization using Proximal Optimization
Sebastian Dalleiger, Jilles Vreeken, Michael Kamp
Identifying informative components in binary data is an essential task in many research areas, including life sciences, social sciences, and recommendation systems. Boolean matrix…
The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective
Nils Philipp Walter, Linara Adilova, Jilles Vreeken +1
Flatness of the loss surface not only correlates positively with generalization, but is also related to adversarial robustness since perturbations of inputs relate non-linearly to…
Learning Exceptional Subgroups by End-to-End Maximizing KL-divergence
Sascha Xu, Nils Philipp Walter, Janis Kalofolias +1
Finding and describing sub-populations that are exceptional regarding a target property has important applications in many scientific disciplines, from identifying disadvantaged de…
Succinct Interaction-Aware Explanations
Sascha Xu, Joscha Cüppers, Jilles Vreeken
SHAP is a popular approach to explain black-box models by revealing the importance of individual features. As it ignores feature interactions, SHAP explanations can be confusing up…
Data is Moody: Discovering Data Modification Rules from Process Event Logs
Marco Bjarne Schuster, Boris Wiegand, Jilles Vreeken
Although event logs are a powerful source to gain insight about the behavior of the underlying business process, existing work primarily focuses on finding patterns in the activity…
Finding Interpretable Class-Specific Patterns through Efficient Neural Search
Nils Philipp Walter, Jonas Fischer, Jilles Vreeken
Discovering patterns in data that best describe the differences between classes allows to hypothesize and reason about class-specific mechanisms. In molecular biology, for example,…