28.04.2023
MCML at ICLR 2023
Three Accepted Papers
11th International Conference on Learning Representations, Kigali, Rwanda, May 01-05, 2023
We are happy to announce that MCML researchers have contributed a total of 3 papers to ICLR 2023. Congrats to our researchers!
Main Track (3 papers)
D. Frauen • S. Feuerriegel
Estimating individual treatment effects under unobserved confounding using binary instruments.
ICLR 2023 - 11th International Conference on Learning Representations. Kigali, Rwanda, May 01-05, 2023. URL
Estimating individual treatment effects under unobserved confounding using binary instruments.
ICLR 2023 - 11th International Conference on Learning Representations. Kigali, Rwanda, May 01-05, 2023. URL
R. Paolino • A. Bojchevski • S. Günnemann • G. Kutyniok • R. Levie
Unveiling the Sampling Density in Non-Uniform Geometric Graphs.
ICLR 2023 - 11th International Conference on Learning Representations. Kigali, Rwanda, May 01-05, 2023. URL
Unveiling the Sampling Density in Non-Uniform Geometric Graphs.
ICLR 2023 - 11th International Conference on Learning Representations. Kigali, Rwanda, May 01-05, 2023. URL
T. Pielok • B. Bischl • D. Rügamer
Approximate Bayesian Inference with Stein Functional Variational Gradient Descent.
ICLR 2023 - 11th International Conference on Learning Representations. Kigali, Rwanda, May 01-05, 2023. URL
Approximate Bayesian Inference with Stein Functional Variational Gradient Descent.
ICLR 2023 - 11th International Conference on Learning Representations. Kigali, Rwanda, May 01-05, 2023. URL
Related
05.02.2026
Needle in a Haystack: Finding Exact Moments in Long Videos
ECCV 2024 research introduces RGNet, an AI model that finds exact moments in long videos using unified retrieval and grounding.
04.02.2026
Benjamin Busam Leads Design of Bavarian Earth Observation Satellite Network “CuBy”
Benjamin Busam leads the scientific design of the “CuBy” satellite network, delivering AI-ready Earth observation data for Bavaria.
©Florian Generotzky / LMU
30.01.2026
Cracks in the Foundations of Cosmology
Daniel Grün examines cosmological tensions that challenge the Standard Model and may point toward new physics.
