Jilles Vreeken
32 papers · 2014–2026 · 8 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (8) π Academic Marathon (11) π Interdisciplinary Bridge π§ Keyword Pioneer π Cross-Pollinator (11)
π
Cross-Pollinator
(11)
π
Renaissance Researcher
(11)
πΊοΈ
Taxonomy Completionist
(65)
π¬
Deep Specialist
(10)
π
Triple Crown
π
Keyword Champion
(2)
π
Grand Slam
β
The Questioner
(2)
ποΈ
Keyword Collector
(139)
π₯
Unstoppable
(7)
π
Century Club
(30)
β‘
Prolific Year
(6)
Conferences
AAAI (15)
ICML (6)
AISTATS (4)
NIPS (3)
EMNLP (1)
ICLR (1)
IJCAI (1)
UAI (1)
Top co-authors
Research topics
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
Whatβs 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