The UAI research group at TU/e explores uncertainty in AI and machine learning from multiple angles on principles of AI, theories of representation, probabilistic AI models, algorithms for learning, reasoning and decision making. There is also an important focus on approaches that are not only accurate but efficient, interpretative, robust and trustworthy.
[Jun/2021] Erik Quaeghebeur is one of the authors of “Wind farm layout optimization using pseudo-gradients” published in Wind Energy Science.
[Apr/2021] Robert Peharz is one of the organisers of the Workshop Tractable Probabilistic Modeling accepted and to be hosted at the 37th Conference on Uncertainty in Artificial Intelligence.
[Apr/2021] “Novel AI driven approach to General Movement Assessment” accepted at Scientific Reports (Springer Nature).
Probabilistic Graphical Models, Imprecise Probability, Computational Complexity, Causality, Robustness, Efficiency, Interpretability
Artificial Intelligence, Imprecise Probability, Surrogate Modelling
Machine Learning, Reinforcement Learning, Fairness-aware Learning, Counterfactual Learning
Artificial Intelligence, Probabilistic Machine Learning, Probabilistic Circuits
Machine Learning, Probabilistic Circuits, Natural Language Processing, Reinforcement Learning
Bayesian Machine Learning, Probabilistic Circuits, Quantitative Finance, Logic, Causality
Generative Models, Probabilistic Circuits, Machine Learning, Neural-Symbolic Integration
Imprecise Probabilities, Probabilistic Graphical Models, Stochastic Processes, Inference Algorithms
Artificial Intelligence, Probabilistic Machine Learning, Active Learning, Ensemble Learning
Artificial Intelligence, Verification, Programming (C++, Python, Java)