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.
The group has been created recently and is growing. We expect to reach around 10 members by the first quarter of 2021. You can keep informed about job opportunities using TU/e vacancies.
[Aug/2020] A Structured View on Weighted Counting with Relations to Counting, Quantum Computation and Applications has been accepted for publication in Information and Computation.
[Aug/2020] Sum-Product Network Decompilation, “Almost No News on the Complexity of MAP in Bayesian Networks”, and Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures have been accepted at PGM 2020.
[Jul/2020] Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits has been presented at ICML 2020.
Machine Learning, Probabilistic Circuits, Natural Language Processing, Reinforcement Learning
Probabilistic Graphical Models, Imprecise Probability, Computational Complexity, Causality, Robustness, Efficiency, Interpretability
Bayesian Machine Learning, Probabilistic Circuits, Quantitative Finance, Logic, Causality
Artificial Intelligence, Imprecise Probability, Surrogate Modelling
Machine Learning, Reinforcement Learning, Fairness-aware Learning, Counterfactual Learning
Artificial Intelligence, Probabilistic Machine Learning, Probabilistic Circuits