4 papers
DISCO: Diversifying Sample Condensation for Efficient Model Evaluation
Alexander Rubinstein, Benjamin Raible, Martin Gubri +1
Evaluating modern machine learning models has become prohibitively expensive. Benchmarks such as LMMs-Eval and HELM demand thousands of GPU hours per model. Costly evaluation reduc…
Are We Done with Object-Centric Learning?
Alexander Rubinstein, Ameya Prabhu, Matthias Bethge +1
Object-centric learning (OCL) seeks to learn representations that only encode an object, isolated from other objects or background cues in a scene. This approach underpins various…
Scalable Ensemble Diversification for OOD Generalization and Detection
Alexander Rubinstein, Luca Scimeca, Damien Teney +1
Training a diverse ensemble of models has several practical applications such as providing candidates for model selection with better out-of-distribution (OOD) generalization, and…
Towards User-Focused Research in Training Data Attribution for Human-Centered Explainable AI
Elisa Nguyen, Johannes Bertram, Evgenii Kortukov +2
Explainable AI (XAI) aims to make AI systems more transparent, yet many practices emphasise mathematical rigour over practical user needs. We propose an alternative to this model-c…