relAI research will be featured at the International Conference on Learning Representations (ICLR), which will take place this year at the Riocentro Convention and Event Center in Rio de Janeiro, Brazil, from April 23rd to 27th, 2026. ICLR is one of the leading conferences with significant impact and reputation in machine learning and artificial intelligence research.

relAI Publications at ICLR

• Seventeen publications from relAI will be presented at the conference, fifteen of them in the main track.
• Two of the relAI papers have been selected for presentation in the Journal-to-Conference Track. Those papers, titled Adversarial Robustness of Graph Transformers and Online Selective Conformal Prediction: Errors and Solutions, were previously published in the Transactions on Machine Learning Research (TMLR) journal.

Meet relAI Students

If you attend ICLR, be sure to take the opportunity to discuss relAI research with relAI students attending the conference: Sarah Ball, Cecilia Casolo, Lukas Gosch, Valentyn Melnychuk, Ole Petersen, Yusuf Sale, Yan Scholten, Jonas von Berg, and Jingpei Wu. You can find their research papers in the list below.

Full list of relAI publications at ICLR 2026:

Main Track



1. Efficient Credal Prediction through Decalibration
   Paul Hofman, Timo Löhr, Maximilian Muschalik, Yusuf Sale, Eyke Hüllermeier
2. Discrete Bayesian Sample Inference for Graph Generation
   Ole Petersen, Marcel Kollovieh, Marten Lienen, Stephan Günnemann
3. Identifiability Challenges in Sparse Linear Ordinary Differential Equations
   Cecilia Casolo, Sören Becker, Niki Kilbertus
4. Sampling-aware Adversarial Attacks Against Large Language Models
   Tim Beyer, Yan Scholten, Leo Schwinn, Stephan Günnemann
5. Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs
   Yan Scholten, Sophie Xhonneux, Leo Schwinn, Stephan Günnemann
6. Efficient and Sharp Off-Policy Learning under Unobserved Confounding
   Konstantin Hess, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
7. Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation
   Valentyn Melnychuk, Dennis Frauen, Jonas Schweisthal, Stefan Feuerriegel
8. GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
   Valentyn Melnychuk, Stefan Feuerriegel
9. IGC-Net for conditional average potential outcome estimation over time
   Konstantin Hess, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
10. On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment
    Sarah Ball, Greg Gluch, Shafi Goldwasser, Frauke Kreuter, Omer Reingold, Guy N. Rothblum
11. Foundation Models for Causal Inference via Prior-Data Fitted Networks
    Yuchen Ma, Dennis Frauen, Emil Javurek, Stefan Feuerriegel
12. An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
    Emil Javurek, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess, Dennis Frauen, Stefan Feuerriegel
13. The Price of Robustness: Stable Classifiers Need Overparameterization
    Jonas von Berg, Adalbert Fono, Massimiliano Datres, Sohir Maskey, Gitta Kutyniok

Journal Track



1. Adversarial Robustness of Graph Transformers
   Philipp Foth, Simon Geisler, Lukas Gosch, Leo Schwinn, Stephan Günnemann
   Transactions on Machine Learning Research (TMLR), Journal Track Poster - ICLR 2026, 2025
   
2. Online Selective Conformal Prediction: Errors and Solutions
   Yusuf Sale, Aaditya Ramdas
   Transactions on Machine Learning Research (TMLR), Journal Track Poster - ICLR 2026, 2025
   

Workshops

1. Exact Certification of Neural Networks and Partition Aggregation Ensembles against Label Poisoning
   Ajinkya Mohgaonkar, Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann
   ICLR 2026 Workshop on Principled Design for Trustworthy AI
   
2. ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward
   Jingpei Wu, Xiao Han, Weixiang Shen, Boer Zhang, Zifeng Ding, Volker Tresp
   ICLR 2026 Workshop on Logical Reasoning of Large Language Models
   

The excellent work of relAI students will be prominently represented at the Thirteenth International Conference on Learning Representations (ICLR) 2025, which will take place at the Singapore EXPO from 24 to 28 April 28 2025.

Thirteen publications from our students will be presented at the conference, nine of them in the main track. Notably, four out of these nine publications have been selected for Oral or Spotlight presentations. This is a significant achievement and demonstrates the high quality of relAI research, considering that only 15% of accepted papers are invited to give a talk.

If you plan to attend the conference, do not miss the opportunity to discuss these publications directly with some of our students. Be sure to attend the Oral presentation by Yan Scholten titled "A Probabilistic Perspective on Unlearning and Alignment for Large Language Models" on the 24^th April. You can learn about Lisa Wimmer´s work, "Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning" at the Workshop on Spurious Correlation and Shortcut Learning: Foundations and Solutions. Additionally, check out the posters of Amine Ketata and Chengzhi Hu!.  Amine will be presenting his work on “Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space” and you can talk to Chengzhi Hu about “Surgical, Cheap, and Flexible: Mitigating False Refusal in Language Models via Single Vector Ablation”.

Full list of relAI publications at ICLR 2025:

Oral Presentation - Main Track

1. A Probabilistic Perspective on Unlearning and Alignment for Large Language Models
   Yan Scholten, Stephan Günnemann, Leo Schwinn

Spotlight Presentation - Main Track

2. Exact Certification of (Graph) Neural Networks Against Label Poisoning
   Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann, Debarghya Ghoshdastidar
3. Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning
   Yan Scholten, Stephan Günnemann
4. Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
   Georg Manten, Cecilia Casolo, Emilio Ferrucci, Søren Wengel Mogensen, Cristopher Salvi, Niki Kilbertus

Posters - Main Track

5. Differentially private learners for heterogeneous treatment effects
   Maresa Schröder, Valentyn Melnychuk, Stefan Feuerriegel
6. Surgical, Cheap, and Flexible: Mitigating False Refusal in Language Models via Single Vector Ablation
   Xinpeng Wang, Chengzhi Hu, Paul Röttger, Barbara Plank
7. ParFam -- (Neural Guided) Symbolic Regression via Continuous Global Optimization
   Philipp Scholl, Katharina Bieker, Hillary Hauger, Gitta Kutyniok
8. Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
   Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer, Tom Wollschläger, Stephan Günnemann
9. Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets
   Yuxin Wang, Maresa Schröder, Dennis Frauen, Jonas Schweisthal, Konstantin Hess, Stefan Feuerriegel

Workshops

10. Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning
    Lisa Wimmer, Bernd Bischl, Ludwig Bothmann
    Workshop on Spurious Correlation and Shortcut Learning: Foundations and Solutions
    
11. Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
    Jan Schuchardt, Mina Dalirrooyfard, Jed Guzelkabaagac, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
    Workshop on Advances in Financial AI: Opportunities, Innovations and Responsible AI
    
12. Cracking the Code: Evaluating Zero-Shot Prompting Methods for Providing Programming Feedback
    Niklas Ippisch, Anna-Carolina Haensch, Markus Herklotz, Jan Simson, Jacob Beck, Malte Schierholz
    Workshop on Human-AI Coevolution
    
13. Exact Certification of (Graph) Neural Networks Against Label Poisoning
    Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann, Debarghya Ghoshdastidar
    VerifAI: AI Verification in the Wild
    
    Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall
    Christopher Bülte, Sohir Maskey, Philipp Scholl, Jonas Berg, Gitta Kutyniok
    Workshop on Tackling Climate Change with Machine Learning