DISCO: Diversifying Sample Condensation for Efficient Model Evaluation

Researchers introduce DISCO, a new method for efficiently evaluating machine learning models by selecting samples that maximize disagreement between models rather than relying on complex clustering approaches. The technique achieves state-of-the-art results in performance prediction while reducing the computational cost of model evaluation.