← Authors

Jilles Vreeken

32 papers · 2014–2026 · 8 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (8) πŸƒ Academic Marathon (11) 🐝 Cross-Pollinator (11) 🌈 Renaissance Researcher (11) πŸ—ΊοΈ Taxonomy Completionist (65) πŸ”¬ Deep Specialist (10) πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) πŸ† Grand Slam ❓ The Questioner (2) πŸ—ƒοΈ Keyword Collector (139) πŸ”₯ Unstoppable (7) πŸ’Ž Century Club (30) ⚑ Prolific Year (6)

Conferences

AAAI (15) ICML (6) AISTATS (4) NIPS (3) EMNLP (1) ICLR (1) IJCAI (1) UAI (1)

Top co-authors

David Kaltenpoth (6) Sebastian Dalleiger (5) Joscha CΓΌppers (4) Michael Kamp (4) Sascha Xu (4) Sarah Mameche (4) Jonas Fischer (4) Alexander Marx (3) Dietrich Klakow (3) Osman Mian (3)

Research topics

Differential Privacy (1) Privacy (1) Statistics (1)

Keywords

causal discovery (12) minimum description length (12) event sequence (4) causal inference (4) pattern discovery (4) causal graph (3) maximum entropy (2) federated learning (2) sequential pattern mining (2) statistical learning (2) causal network (2) boolean matrix factorization (2) kolmogorov complexity (2) multi-environment learning (2) unsupervised learning (2) graph matching (1) graph classification (1) parameter estimation (1) graph clustering (1) graph structure (1)

Papers

Causal Discovery from Interval-Based Event Sequences AAAI 2026
SEQRET: Mining Rule Sets from Event Sequences AAAI 2026
Information-Theoretic Causal Discovery in Topological Order AISTATS 2025
From Your Block to Our Block: How to Find Shared Structure Between Stochastic Block Models over Multiple Graphs AAAI 2025
SPACETIME: Causal Discovery from Non-Stationary Time Series AAAI 2025
Federated Binary Matrix Factorization Using Proximal Optimization AAAI 2025
What Are the Rules? Discovering Constraints from Data AAAI 2024
Learning Exceptional Subgroups by End-to-End Maximizing KL-Divergence ICML 2024
Causal Discovery from Event Sequences by Local Cause-Effect Attribution NIPS 2024
Discovering Sequential Patterns with Predictable Inter-event Delays AAAI 2024
Finding Interpretable Class-Specific Patterns through Efficient Neural Search AAAI 2024
Identifying Confounding from Causal Mechanism Shifts AISTATS 2024
Federated Learning from Small Datasets ICLR 2023
Identifying Selection Bias from Observational Data AAAI 2023
Learning Causal Models under Independent Changes NIPS 2023
Nonlinear Causal Discovery with Latent Confounders ICML 2023
Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition UAI 2023
Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments AAAI 2023
Towards Concept-Aware Large Language Models EMNLP 2023
Nothing but Regrets β€” Privacy-Preserving Federated Causal Discovery AISTATS 2023
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent NIPS 2022
Differentially Describing Groups of Graphs AAAI 2022
Naming the Most Anomalous Cluster in Hilbert Space for Structures with Attribute Information AAAI 2022
Discovering Interpretable Data-to-Sequence Generators AAAI 2022
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors ICML 2022
Inferring Cause and Effect in the Presence of Heteroscedastic Noise ICML 2022
What’s in the Box? Exploring the Inner Life of Neural Networks with Robust Rules ICML 2021
Discovering Fully Oriented Causal Networks AAAI 2021
Explainable Data Decompositions AAAI 2020
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms IJCAI 2019
Testing Conditional Independence on Discrete Data using Stochastic Complexity AISTATS 2019
Multivariate Maximal Correlation Analysis ICML 2014