David Steinmann — accepted AI-conference papers

2026

Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings

Berkant Turan, Suhrab Asadulla, David Steinmann, Kristian Kersting, Wolfgang Stammer, Sebastian Pokutta

ICML 2026spotlight

While *Prover-Verifier Games* (PVGs) offer a promising path toward verifiability in nonlinear classification models, they have not yet been applied to complex inputs such as high-dimensional images. Conversely, expressive *concept encodings* effectively allow to translate such data into interpretabl…

Cited by 0 Source ↗Scholar ↗

2025

Object-Centric Concept-Bottlenecks

David Steinmann, Wolfgang Stammer, Antonia Wüst, Kristian Kersting

NeurIPS 2025poster

Developing high-performing, yet interpretable models remains a critical challenge in modern AI. Concept-based models (CBMs) attempt to address this by extracting human-understandable concepts from a global encoding (e.g., image encoding) and then applying a linear classifier on the resulting concept…

Cited by 0 Source ↗Scholar ↗

2024

Learning to Intervene on Concept Bottlenecks

David Steinmann, Wolfgang Stammer, Felix Friedrich, Kristian Kersting

ICML 2024poster

While deep learning models often lack interpretability, concept bottleneck models (CBMs) provide inherent explanations via their concept representations. Moreover, they allow users to perform interventional interactions on these concepts by updating the concept values and thus correcting the predict…

Cited by 19 Source ↗PDF ↗code ↗Scholar ↗

2024

Neural Concept Binder

Wolfgang Stammer, Antonia Wüst, David Steinmann, Kristian Kersting

NeurIPS 2024poster

The challenge in object-based visual reasoning lies in generating concept representations that are both descriptive and distinct. Achieving this in an unsupervised manner requires human users to understand the model's learned concepts and, if necessary, revise incorrect ones. To address this challen…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗