SOGAR learns Pareto-optimal recourse summaries by solving a bi-objective decision tree problem, yielding stable low-cost effective group actions that outperform prior methods on effectiveness and cost.
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A Unified Approach to Interpreting Model Predic- tions, November 2017
19 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.
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cs.LG 5 cs.AI 3 cs.CR 2 cs.SE 2 astro-ph.GA 1 astro-ph.SR 1 cs.CL 1 cs.CV 1 cs.HC 1 eess.IV 1years
2026 19roles
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background 1representative citing papers
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A new sample of young candidate Bridge stars is identified and shown to align with gas structures, with kinematics implying a ~125 Myr crossing time consistent with the last LMC-SMC interaction.
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A surrogate modeling method approximates LLM-encoded medical knowledge via prompting to quantify variable influence and flag inaccuracies and racial biases.
An optimized KernelSHAP method for 3D medical image segmentation restricts computation to ROI and receptive fields, uses patch logit caching for 15-30% savings, and compares organ units versus supervoxels for clinically interpretable attributions.
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A conditional Wasserstein GAN generates plausible future SWI drought trajectories for French insurance risk management under climate change.
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