A sparse top-1 mixture of linear experts, trained with SAC and distilled into decision trees, matches or beats interpretable baselines and narrows the gap to opaque policies on MuJoCo tasks.
A Comparative Study of Faithfulness Metrics for Model Interpretability Methods
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abstract
Interpretation methods to reveal the internal reasoning processes behind machine learning models have attracted increasing attention in recent years. To quantify the extent to which the identified interpretations truly reflect the intrinsic decision-making mechanisms, various faithfulness evaluation metrics have been proposed. However, we find that different faithfulness metrics show conflicting preferences when comparing different interpretations. Motivated by this observation, we aim to conduct a comprehensive and comparative study of the widely adopted faithfulness metrics. In particular, we introduce two assessment dimensions, namely diagnosticity and time complexity. Diagnosticity refers to the degree to which the faithfulness metric favours relatively faithful interpretations over randomly generated ones, and time complexity is measured by the average number of model forward passes. According to the experimental results, we find that sufficiency and comprehensiveness metrics have higher diagnosticity and lower time complexity than the other faithfulness metric
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cs.LG 1years
2024 1verdicts
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SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks
A sparse top-1 mixture of linear experts, trained with SAC and distilled into decision trees, matches or beats interpretable baselines and narrows the gap to opaque policies on MuJoCo tasks.