02.08.2024
MCML at IJCAI 2024
Three Accepted Papers (2 Main, and 1 Workshop)
33rd International Joint Conference on Artificial Intelligence, Jeju, Korea, Aug 03-09, 2024
We are happy to announce that MCML researchers have contributed a total of 3 papers to IJCAI 2024: 2 Main, and 1 Workshop papers. Congrats to our researchers!
Main Track (2 papers)
J. Brandt • M. Wever • V. Bengs • E. Hüllermeier
Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO.
IJCAI 2024 - 33rd International Joint Conference on Artificial Intelligence. Jeju, Korea, Aug 03-09, 2024. DOI
Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO.
IJCAI 2024 - 33rd International Joint Conference on Artificial Intelligence. Jeju, Korea, Aug 03-09, 2024. DOI
J. G. Wiese • L. Wimmer • T. Papamarkou • B. Bischl • S. Günnemann • D. Rügamer
Towards Efficient Posterior Sampling in Deep Neural Networks via Symmetry Removal (Extended Abstract).
IJCAI 2024 - 33rd International Joint Conference on Artificial Intelligence. Jeju, Korea, Aug 03-09, 2024. DOI
Towards Efficient Posterior Sampling in Deep Neural Networks via Symmetry Removal (Extended Abstract).
IJCAI 2024 - 33rd International Joint Conference on Artificial Intelligence. Jeju, Korea, Aug 03-09, 2024. DOI
Workshops (1 paper)
P. Wicke • L. Hirlimann • J. M. Cunha
Using Analogical Reasoning to Prompt LLMs for their Intuitions of Abstract Spatial Schemas.
Analogy-ANGLE 2024 @IJCAI 2024 - 1st Workshop on Analogical Abstraction in Cognition, Perception, and Languageat the 33rd International Joint Conference on Artificial Intelligence. Jeju, Korea, Aug 03-09, 2024. PDF
Using Analogical Reasoning to Prompt LLMs for their Intuitions of Abstract Spatial Schemas.
Analogy-ANGLE 2024 @IJCAI 2024 - 1st Workshop on Analogical Abstraction in Cognition, Perception, and Languageat the 33rd International Joint Conference on Artificial Intelligence. Jeju, Korea, Aug 03-09, 2024. PDF
Related
26.08.2026
Transparency for Global Health Aid
MCML PI Stefan Feuerriegel and his team use machine learning to uncover disparities in the global allocation of health aid.
25.08.2026
David Rügamer: We Need Uncertainty Quantification
MCML PI David Rügamer explains why uncertainty quantification is essential for building trustworthy AI and making informed decisions.
