We are happy to announce that relAI will be represented in Seoul, South Korea, from July 6 to 11 at the Forty-Third International Conference on Machine Learning (ICML2026)!. ICML is widely recognized as one of the top three most influential conferences in machine learning and artificial intelligence research.
📖 About relAI Publications at ICML
- relAI contributes fourteen publications to the conference, including nine in the main track.
- relAI PhD student Emre Kavak will present a Spotlight paper (ranked in the top 2.2%).
- Five relAI papers will be presented at ICML Workshops, with contributions from three relAI MSc students.
🤝Meet relAI Students
If you are attending ICML we encourage you to engage in discussions about relAI research with the following relAI students present at the conference:
- relAI PhD Students: Emre Kavak, Maedeh Zarvandi, Tzu-Yuan Huang,Julius Hege, Sarah Ball, Maria Matveev, Valentyn Melnychuk, Tim Tomov, Chengzhi (Martin) Hu, and Tong Liu
- relAI MSc Students: Sofiia Nikolenko, Fabian Grob and Manuel Hülskamp
You can find their research papers in the list below.
Full list of relAI publications at ICML 2026:
- DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlation
Emre Kavak, Tom Nuno Wolf, Christian Wachinger - Certifying Graph Neural Networks Against Label and Structure Poisoning
Lukas Gosch, Xichuan Chen, Yan Scholten, Stephan Günnemann - Rank-Learner: Orthogonal Ranking of Treatment Effects
Henri Arno, Dennis Frauen, Emil Javurek, Thomas Demeester, Stefan Feuerriegel - Interpretable Self-Supervised Learning via Representer Landmarks and Nyström Approximation
Maedeh Zarvandi, Michael Timothy, Theresa Wasserer, Debarghya Ghoshdastidar - SAD-Flower: Flow Matching for Safe, Admissible, and Dynamically Consistent Planning
Tzu-Yuan Huang, Armin Lederer, Dai-Jie Wu, Xiaobing Dai, Sihua Zhang, Hsiu-Chin Lin, Shao-Hua Sun, Stefan Sosnowski, Sandra Hirche - Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference
Valentyn Melnychuk, Vahid Balazadeh, Stefan Feuerriegel, Rahul G. Krishnan - Reading Between the Tokens: Improving Preference Predictions through Mechanistic Forecasting
Sarah Ball, Simeon Allmendinger, Niklas Kühl, Frauke Kreuter - Don't Walk the Line: Boundary Guidance for Filtered Generation
Sarah Ball, Andreas Haupt - ExPLAIND: Unifying Model, Data, and Training Attribution to Study Model Behavior
Florian Eichin, Yupei Du, Philipp Mondorf, Maria Matveev, Barbara Plank, Michael A. Hedderich - Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization
Vit Fojtik, Maria Matveev, Hung-Hsu Chou, Gitta Kutyniok, Johannes Maly
Spotlight Presentation - Main Track
Posters - Main Track
- Proxy Scoring Enables Benchmarking LLM Forecasters Without Waiting for Outcomes
Julius Hege, Gitta Kutyniok
ICML 2026, Forecasting as a New Frontier of Intelligence Workshop - What Intermediate Layers Know: Detecting Jailbreaks from Entropy Dynamics
Sofiia Nikolenko, Michele Papucci, Mina Rezaei, Shireen Kudukkil Manchingal
ICML 2026, The 2nd Workshop on Epistemic Intelligence in Machine Learning - Bigger Is Not Better: Inverse Scaling and Arbitration Failure in Counterfactual Visual Grounding
Fabian Grob, Sanghwan Kim, Cordelia Schmid, Zeynep Akata
ICML 2026, Mechanistic Interpretability Workshop - On the Uncertainty in Prior-Data Fitted Network Pretraining
Manuel Hülskamp, Julius Kobialka, Emanuel Sommer, David Rügamer
ICML 2026, 2nd Workshop on Foundation Models for Structured Data (FMSD) - ExPLAIND: Unifying Model, Data, and Training Attribution to Study Model Behavior
Florian Eichin, Yupei Du, Philipp Mondorf, Maria Matveev, Barbara Plank, Michael A. Hedderich
ICML 2026, Mechanistic Interpretability Workshop
Workshops
