ELLIS Institute Tübingen at ICML 2025

The 42nd International Conference on Machine Learning (ICML) will take place in Vancouver, Canada, from July 13 to 19, 2025.

This premier global event unites leading experts focused on advancing machine learning, a pivotal area within artificial intelligence.

ICML is renowned for showcasing and publishing innovative research that spans all facets of machine learning, intersecting closely with fields such as artificial intelligence, statistics, and data science. It also highlights key applications including computer vision, computational biology, speech processing, and robotics.

At ICML 2025, the ELLIS Institute Tübingen will showcase innovative research findings, highlighting its commitment to driving progress in machine learning.

Below is the list of contributions from the ELLIS Institute Tübingen Principal Investigators (highlighted in bold).
 

Title	List of Authors
Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models	Armin Kekić, Sergio Garrido Mejia, Bernhard Schölkopf
Generative Intervention Models for Causal Perturbation Modeling	Nora Schneider, Lars Lorch, Niki Kilbertus, Bernhard Schölkopf, Andreas Krause
Generalized Interpolating Discrete Diffusion	Dimitri von Rütte, Janis Fluri, Yuhui Ding, Antonio Orvieto, Bernhard Schölkopf, Thomas Hofmann
LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws	Prasanna Mayilvahanan, Thaddäus Wiedemer, Sayak Mallick, Matthias Bethge, Wieland Brendel
LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models	Fanfei Li, Thomas Klein, Wieland Brendel, Robert Geirhos, Roland S. Zimmermann
Position: An Empirically Grounded Identifiability Theory Will Accelerate Self Supervised Learning Research	Patrik Reizinger, Randall Balestriero, David Klindt, Wieland Brendel
When, Where and Why to Average Weights?	Niccolò Ajroldi, Antonio Orvieto, Jonas Geiping
An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks	Valentyn Boreiko, Alexander Panfilov, Václav Voráček, Matthias Hein, Jonas Geiping
Great Language Models Think Alike and this Undermines AI Oversight	Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Pandurang Prabhu, Matthias Bethge, Jonas Geiping
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted Networks	Dongwoo Lee, Dong Bok Lee, Steven Adriaensen, Juho Lee, Sung Ju Hwang, Frank Hutter, Seon Joo Kim, Hae Beom Lee
FairPFN: A Tabular Foundation Model for Causal Fairness	Jake Robertson, Noor Awad, Noah Hollmann, Frank Hutter, Samuel Gabriel Müller
Tuning LLM Judge Design Decisions for 1/1000 of the Cost	David Salinas, Omar Swelam, Frank Hutter
Position: The Future of Bayesian Prediction Is Prior-Fitted	Samuel Gabriel Müller, Arik Reuter, Noah Hollmann, David Rügamer, Frank Hutter
From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications	Ajay Jaiswal, Yifan Wang, Lu Yin, Shiwei Liu, Runjin Chen, Jiawei Zhao, Ananth Grama, Yuandong Tian, Zhangyang “Atlas” Wang
LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning	Zihang Liu, Tianyu Pang, Oleg Balabanov, Chaoqun Yang, Tianjin Huang, Lu Yin, Yaoqing Yang, Shiwei Liu
Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn More	Xialie Zhuang, Zhikai Jia, Jianjin Li, Zhenyu Zhang, Li Shen, Zheng Cao, Shiwei Liu

More about the Conference