10 papers
We Need Explanation Cards to Connect Explanation Algorithms to the Real World
Eric Günther, Balázs Szabados, Kristof Meding +3
Algorithmic explanations are intended to help stakeholders understand opaque algorithmic decisions, but in practice, they often fall short. First, the meaning of algorithmic explan…
Validity Threats for Foundation Model Research
Gunnar König, Martin Pawelczyk, Ulrike von Luxburg +1
Controlled experiments are the backbone of machine learning research, but at the scale of modern foundation models, they have become prohibitively expensive. Instead, the community…
Performative Validity of Recourse Explanations
Gunnar König, Hidde Fokkema, Timo Freiesleben +2
When applicants get rejected by an algorithmic decision system, recourse explanations provide actionable suggestions for how to change their input features to get a positive evalua…
On the Surprising Effectiveness of Large Learning Rates under Standard Width Scaling
Moritz Haas, Sebastian Bordt, Ulrike von Luxburg +1
Scaling limits, such as infinite-width limits, serve as promising theoretical tools to study large-scale models. However, it is widely believed that existing infinite-width theory…
Informative Post-Hoc Explanations Only Exist for Simple Functions
Eric Günther, Balázs Szabados, Robi Bhattacharjee +2
Many researchers have suggested that local post-hoc explanation algorithms can be used to gain insights into the behavior of complex machine learning models. However, theoretical g…
How Much Can We Forget about Data Contamination?
Sebastian Bordt, Suraj Srinivas, Valentyn Boreiko +1
The leakage of benchmark data into the training data has emerged as a significant challenge for evaluating the capabilities of large language models (LLMs). In this work, we challe…