Home | News

19.01.2026

Tiny logo
Teaser image to MCML at AAAI 2026

MCML at AAAI 2026

19 Accepted Papers (14 Main, and 5 Workshops)

40th Conference on Artificial Intelligence, Singapore, Jan 20-27, 2026

We are happy to announce that MCML researchers have contributed a total of 19 papers to AAAI 2026: 14 Main, and 5 Workshop papers. Congrats to our researchers!

Main Track (14 papers)

B. BühlerI. BuenoE. Kasneci
Democratizing Writing Support with AI: Insights from One Year of Real-World Interactions with an Open-Access Writing Feedback Tool.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI GitHub

H. Chen • J. Li • Y. ZhangJ. Bi • Y. Xia • J. Gu • V. Tresp
AUVIC: Adversarial Unlearning of Visual Concepts for Multi-modal Large Language Models.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI

C. Fiedler
Statistical Learning Theory for Distributional Classification.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI

P. HofmanY. SaleE. Hüllermeier
Uncertainty Quantification for Machine Learning: One Size Does Not Fit All.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI

X. Jian • P. Zhang • L. Tian • F. Ji • W. Liang • W. P. Tay • B. Wen • F. Krahmer
Conformal Prediction for Multi-Source Detection on a Network.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI

Y. Lei • X. Ge • Y. Zhang • Y. Yang • B. Ma
Do Large Language Models Think like the Brain? Sentence-Level Evidences from Layer-Wise Embeddings and fMRI.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI

G. D. Pelegrina • P. KolpaczkiE. Hüllermeier
Shapley Value Approximation Based on k-Additive Games.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI

Y. Wang •  Aniri • J. Bi • S. Pirk • Y. Ma
ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI

N. Walha • S. G. Gruber • T. Decker • Y. Yang • A. JavanmardiE. Hüllermeier • F. Buettner
Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI

S. Wang • J. He • N. Andreo • X. Zhu
GEWDiff: Geometric Enhanced Wavelet-based Diffusion Model for Hyperspectral Image Super-resolution.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI

M. Wever • M. MuschalikF. Fumagalli • M. Lindauer
HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI

M. Wei • K. Yuan • S. Li • Y. ZhouL. BaiN. Navab • H. Ren • H. J. Lee • T. Vercauteren • N. Padoy
Where It Moves, It Matters: Referring Surgical Instrument Segmentation via Motion.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI

Y. Yeganeh • G. Guvercin • N. NavabA. Farshad
Conformable Convolution for Topologically Constrained Learning of Complex Anatomical Structures.
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI GitHub

W. Zhang • Q. Cheng • D. Skuddis • N. Zeller • D. Cremers • N. Haala
HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction (Abstract Reprint).
AAAI 2026 - 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. DOI GitHub

Workshops (5 papers)

T. DeckerV. Tresp
Towards Quantifying Incompatibilities in Evaluation Metrics for Feature Attributions.
XAI4Science @AAAI 2026 - 2nd Workshop XAI4Science: From Understanding Model Behavior to Discovering New Scientific Knowledge at the 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. To be published. Preprint available. URL

A. FonoG. Kutyniok • H. Boche
How to realize efficient Spiking Neural Networks?
MATH4AI @AAAI 2026 - Workshop on Foretell of Future AI from Mathematical Foundation at the 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. To be published. Preprint available. URL

X. XueX. Zhu
Towards Unified Vision Language Models for Forest Ecological Analysis in Earth Observation.
AI4ES @AAAI 2026 - Workshop on AI for Environmental Science at the 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. To be published. Preprint available. arXiv GitHub

S. Zhao • S. Scepanovic • G. Baranyi • Z. Xiong • D. Quercia • X. Zhu
Ecological Determinants of Antidepressants Prescriptions in England: Using Machine Learning for Causal Discovery.
AI4ES @AAAI 2026 - Workshop on AI for Environmental Science at the 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. To be published. Preprint available. URL

Y. Zhang • S. Tang • Z. Li • Z. Han • V. Tresp
WebArbiter: A Principle-Guided Reasoning Process Reward Model for Web Agents.
LaMAS @AAAI 2026 - Workshop on LLM-based Multi-Agent Systems: Towards Responsible, Reliable, and Scalable Agentic Systems at the 40th Conference on Artificial Intelligence. Singapore, Jan 20-27, 2026. To be published. Preprint available. URL

#research #top-tier-work #cremers #fornasier #fumagalli #huellermeier #kasneci-enkelejda #krahmer #kreuter #kutyniok #navab #schuetze #tresp #zhu

Related

Tiny logo
Link to MCML at ICDAR 2026

28.08.2026

MCML at ICDAR 2026

MCML researchers are represented with 1 paper at ICDAR 2026.

Read more
Link to Digdeep Podcast: Well-Intentioned but Dangerous – Will AI Regulation Become a Tool for Censorship?

27.08.2026

Digdeep Podcast: Well-Intentioned but Dangerous – Will AI Regulation Become a Tool for Censorship?

In this episode of #digdeep, MCML Junior Member Sarah Ball talks together with Phil Hackemann about AI regulation.

Read more
Link to Transparency for global health aid

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.

Read more
Link to David Rügamer: We Need Uncertainty Quantification

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.

Read more
Link to Using Data Science to Personalize Depression Care

24.08.2026

Using Data Science to Personalize Depression Care

Five young researchers explore how data science can contribute to better healthcare as part of DSSGx Munich 2026, supported by MCML.

Read more
Back to Top