18.07.2025
MCML at UAI 2025
Four Accepted Papers
41st Conference on Uncertainty in Artificial Intelligence, Rio de Janeiro, Brazil, Jul 21-25, 2025
We are happy to announce that MCML researchers have contributed a total of 4 papers to UAI 2025. Congrats to our researchers!
Main Track (4 papers)
M. Arpogaus • T. Kneib • T. Nagler • D. Rügamer
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals.
UAI 2025 - 41st Conference on Uncertainty in Artificial Intelligence. Rio de Janeiro, Brazil, Jul 21-25, 2025. URL
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals.
UAI 2025 - 41st Conference on Uncertainty in Artificial Intelligence. Rio de Janeiro, Brazil, Jul 21-25, 2025. URL
M. Drton • M. Garrote-López • N. Nikov • E. Robeva • Y. S. Wang
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles.
UAI 2025 - 41st Conference on Uncertainty in Artificial Intelligence. Rio de Janeiro, Brazil, Jul 21-25, 2025. URL GitHub
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles.
UAI 2025 - 41st Conference on Uncertainty in Artificial Intelligence. Rio de Janeiro, Brazil, Jul 21-25, 2025. URL GitHub
K. Göbler • T. Windisch • M. Drton
Nonlinear Causal Discovery for Grouped Data.
UAI 2025 - 41st Conference on Uncertainty in Artificial Intelligence. Rio de Janeiro, Brazil, Jul 21-25, 2025. URL
Nonlinear Causal Discovery for Grouped Data.
UAI 2025 - 41st Conference on Uncertainty in Artificial Intelligence. Rio de Janeiro, Brazil, Jul 21-25, 2025. URL
J. Hanselle • A. Javanmardi • T. Oberkofler • Y. Sale • E. Hüllermeier
Conformal Prediction without Nonconformity Scores.
UAI 2025 - 41st Conference on Uncertainty in Artificial Intelligence. Rio de Janeiro, Brazil, Jul 21-25, 2025. URL
Conformal Prediction without Nonconformity Scores.
UAI 2025 - 41st Conference on Uncertainty in Artificial Intelligence. Rio de Janeiro, Brazil, Jul 21-25, 2025. URL
Related
16.09.2026
Vincent Fortuin Receives ERC Starting Grant for Reliable Small-Data AI
MCML PI Vincent Fortuin receives an ERC Starting Grant for AutoBayes, advancing reliable AI methods for learning from small datasets.
