Home  | News

28.04.2023

Tiny logo
Teaser image to MCML at ICLR 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. FrauenS. 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

R. Paolino • A. Bojchevski • S. GünnemannG. 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

T. PielokB. BischlD. 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

#research #top-tier-work #bischl #feuerriegel #guennemann #kutyniok #ruegamer

Related

Link to How Should Researchers Report Their Use of LLMs?

10.06.2026

How Should Researchers Report Their Use of LLMs?

Is AI making science impossible to replicate? Stefan Feuerriegel and the MCML team introduce the GUIDE-LLM framework in Nature.

Read more
Link to Benjamin Lange: The Real Risk of AI Agents is Manipulation Through Kindness

02.06.2026

Benjamin Lange: The Real Risk of AI Agents Is Manipulation Through Kindness

MCML Junior Research Group Leader Benjamin Lange examines how trust in AI agents can itself become a source of risk.

Read more
Tiny logo
Link to MCML at CVPR 2026

02.06.2026

MCML at CVPR 2026

MCML researchers are represented with 28 papers at CVPR 2026 (26 Main, and 2 Workshops).

Read more
Tiny logo
Link to MCML at ICRA 2026

29.05.2026

MCML at ICRA 2026

MCML researchers are represented with 4 papers at ICRA 2026 (3 Main, and 1 Workshop).

Read more
Link to Zeynep Akata: To Trust AI, We Need to Understand What Goes On Behind the Scenes

28.05.2026

Zeynep Akata: To Trust AI, We Need to Understand What Goes on Behind the Scenes

MCML PI Zeynep Akata explains that to trust AI, we must understand its inner workings, address foundation model bias, and make explainability central.

Read more
Back to Top