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ProToMEx: Rapid, Interpretable Explanations via Structured Representations
Not provided
explainabilitymachine learningprobabilistic modelsreal-time applications
2609.04265
Builder Relevance
7h ago80%
Abstract
ProToMEx introduces a new paradigm for explainability in machine learning that leverages Probabilistic Topic Models to provide both global and local explanations of model behavior.
Reality Card
Core Claim
ProToMEx achieves explanations of comparable fidelity to SHAP and LIME while being 30-40x faster in generating local explanations.
Method / Result
ProToMEx is ~30-40x faster than SHAP and LIME over standardised tabular datasets.
Limitations
The paper does not specify limitations or reproducibility concerns.
Paper to code
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