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ARXIV:2603.14894 · MODEL EXPLANATIONS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.14894MODEL EXPLANATIONSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
EAGLE is an information-theoretic framework for generating reliable post-hoc explanations of black-box ML models.
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Pain EAGLE is an information-theoretic framework for generating reliable post-hoc explanations of black-box ML models.
Evidence 0 refs | 0 sources | 17% coverage
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EAGLE is an information-theoretic framework for generating reliable post-hoc explanations of black-box ML models. Post-hoc model-agnostic explanation methods addresses this challenge by learning a surrogate model that approximates the behavior of the deployed black-box…
Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model explanations. Post-hoc model-agnostic explanation methods addresses this challenge by learning a surrogate model…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Empirical results on tabular and image datasets corroborate our theoretical findings and demonstrate that \texttt{EAGLE} improves explanation reproducibility across runs, achieves higher neighborhood stability,…
Model Explanations moved forward this cycle; last verified April 2026. Public score 6.0/10.
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EAGLE is an information-theoretic framework for generating reliable post-hoc explanations of black-box ML models.
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10.48550/arXiv.2603.14894EAGLE is an information-theoretic framework for generating reliable post-hoc explanations of black-box ML models.
Abstract
Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model explanations. Post-hoc model-agnostic explanation methods addresses this challenge by learning a surrogate model that approximates the behavior of the deployed black-box ML model in the locality of a sample of interest. In post-hoc scenarios, neither the underlying model parameters nor the training are available, and hence, this local neighborhood must be constructed by generating perturbed inputs in the neighborhood of the sample of interest, and its corresponding model predictions. We propose \emph{Expected Active Gain for Local Explanations} (\texttt{EAGLE}), a post-hoc model-agnostic explanation framework that formulates perturbation selection as an information-theoretic active learning problem. By adaptively sampling perturbations that maximize the expected information gain, \texttt{EAGLE} efficiently learns a linear surrogate explainable model while producing feature importance scores along with the uncertainty/confidence estimates. Theoretically, we establish that cumulative information gain scales as $\mathcal{O}(d \log t)$, where $d$ is the feature dimension and $t$ represents the number of samples, and that the sample complexity grows linearly with $d$ and logarithmically with the confidence parameter $1/δ$. Empirical results on tabular and image datasets corroborate our theoretical findings and demonstrate that \texttt{EAGLE} improves explanation reproducibility across runs, achieves higher neighborhood stability, and improves perturbation sample quality as compared to state-of-the-art baselines such as Tilia, US-LIME, GLIME and BayesLIME.
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PROBLEM
EAGLE is an information-theoretic framework for generating reliable post-hoc explanations of black-box ML models. Post-hoc model-agnostic explanation methods addresses this challenge by learning a surrogate model that approximates the behavior of the deployed black-box ML model...
METHOD
Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model explanations. Post-hoc model-agnostic explanation methods addresses this challenge by learning a surrogate model that approximates the behavi...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Empirical results on tabular and image datasets corroborate our theoretical findings and demonstrate that \texttt{EAGLE} improves explanation reproducibility across runs, achieves higher neighborhood stab...
WHY NOW
Model Explanations moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed public claims while anchored extraction refreshes.
EAGLE is an information-theoretic framework for generating reliable post-hoc explanations of black-box ML models. Post-hoc model-agnostic explanation methods addresses this challenge by learning a surrogate model that approximates the behavior of the deployed black-box ML model in the locality of a sample of interest.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model explanations. Post-hoc model-agnostic explanation methods addresses this challenge by learning a surrogate model that approximates the behavior of the deployed black-box ML model in the locality of a sample of interest.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Empirical results on tabular and image datasets corroborate our theoretical findings and demonstrate that \texttt{EAGLE} improves explanation reproducibility across runs, achieves higher neighborhood stability, and improves perturbation sample quality as compared to state-of-the-art baselines such as Tilia, US-LIME, GLIME and BayesLIME.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Model Explanations moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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EAGLE is an information-theoretic framework for generating reliable post-hoc explanations of black-box ML models.
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