Poster
in
Workshop: Information-Theoretic Principles in Cognitive Systems
Machine Learning Explainability from an Information-theoretic Perspective
Debargha Ganguly · Debayan Gupta
Abstract:
The primary challenge for practitioners with multiple \textit{post-hoc gradient-based} interpretability methods is to benchmark them and select the best. Using information theory, we represent finding the optimal explainer as a rate-distortion optimization problem. Therefore : The adjoining experiments, code and data will be released soon.
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