New at ICML: improving the stability of feature attributions through optimal combinations!

We improve the quality of feature attributions by optimally combining multiple explanation methods. Our convex-combination strategy enhances robustness and faithfulness, consistently outperforming individual methods and baselines across architectures. Published at the International Conference on Machine Learning.