FastSHAP: Real-Time Shapley Value Estimation

• URL: http://arxiv.org/abs/2107.07436v1
• Date: Thu, 15 Jul 2021 16:34:45 GMT
• Title: FastSHAP: Real-Time Shapley Value Estimation
• Authors: Neil Jethani, Mukund Sudarshan, Ian Covert, Su-In Lee, Rajesh Ranganath
• Abstract summary: FastSHAP is a method for estimating Shapley values in a single forward pass using a learned explainer model. It amortizes the cost of explaining many inputs via a learning approach inspired by Shapley value's weighted least squares characterization. It generates high-quality explanations with orders of magnitude speedup.
• Score: 25.536804325758805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shapley values are widely used to explain black-box models, but they are costly to calculate because they require many model evaluations. We introduce FastSHAP, a method for estimating Shapley values in a single forward pass using a learned explainer model. FastSHAP amortizes the cost of explaining many inputs via a learning approach inspired by the Shapley value's weighted least squares characterization, and it can be trained using standard stochastic gradient optimization. We compare FastSHAP to existing estimation approaches, revealing that it generates high-quality explanations with orders of magnitude speedup.

Related papers

• Improving the Sampling Strategy in KernelSHAP [0.8057006406834466]
  KernelSHAP framework enables us to approximate the Shapley values using a sampled subset of weighted conditional expectations. We propose three main novel contributions: a stabilizing technique to reduce the variance of the weights in the current state-of-the-art strategy, a novel weighing scheme that corrects the Shapley kernel weights based on sampled subsets, and a straightforward strategy that includes the important subsets and integrates them with the corrected Shapley kernel weights.
  arXiv  Detail & Related papers  (2024-10-07T10:02:31Z)
• Fast Shapley Value Estimation: A Unified Approach [71.92014859992263]
  We propose a straightforward and efficient Shapley estimator, SimSHAP, by eliminating redundant techniques. In our analysis of existing approaches, we observe that estimators can be unified as a linear transformation of randomly summed values from feature subsets. Our experiments validate the effectiveness of our SimSHAP, which significantly accelerates the computation of accurate Shapley values.
  arXiv  Detail & Related papers  (2023-11-02T06:09:24Z)
• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• Efficient Shapley Values Estimation by Amortization for Text Classification [66.7725354593271]
  We develop an amortized model that directly predicts each input feature's Shapley Value without additional model evaluations. Experimental results on two text classification datasets demonstrate that our amortized model estimates Shapley Values accurately with up to 60 times speedup.
  arXiv  Detail & Related papers  (2023-05-31T16:19:13Z)
• PDD-SHAP: Fast Approximations for Shapley Values using Functional Decomposition [2.0559497209595823]
  We propose PDD-SHAP, an algorithm that uses an ANOVA-based functional decomposition model to approximate the black-box model being explained. This allows us to calculate Shapley values orders of magnitude faster than existing methods for large datasets, significantly reducing the amortized cost of computing Shapley values.
  arXiv  Detail & Related papers  (2022-08-26T11:49:54Z)
• Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search [96.20505710087392]
  We propose a Shapley value based method to evaluate operation contribution (Shapley-NAS) for neural architecture search. We show that our method outperforms the state-of-the-art methods by a considerable margin with light search cost.
  arXiv  Detail & Related papers  (2022-06-20T14:41:49Z)
• Fast Hierarchical Games for Image Explanations [78.16853337149871]
  We present a model-agnostic explanation method for image classification based on a hierarchical extension of Shapley coefficients. Unlike other Shapley-based explanation methods, h-Shap is scalable and can be computed without the need of approximation. We compare our hierarchical approach with popular Shapley-based and non-Shapley-based methods on a synthetic dataset, a medical imaging scenario, and a general computer vision problem.
  arXiv  Detail & Related papers  (2021-04-13T13:11:02Z)
• Shapley Explanation Networks [19.89293579058277]
  We propose to incorporate Shapley values themselves as latent representations in deep models. We operationalize the Shapley transform as a neural network module and construct both shallow and deep networks, called ShapNets. Our Shallow ShapNets compute the exact Shapley values and our Deep ShapNets maintain the missingness and accuracy properties of Shapley values.
  arXiv  Detail & Related papers  (2021-04-06T05:42:12Z)
• Towards Efficient Data Valuation Based on the Shapley Value [65.4167993220998]
  We study the problem of data valuation by utilizing the Shapley value. The Shapley value defines a unique payoff scheme that satisfies many desiderata for the notion of data value. We propose a repertoire of efficient algorithms for approximating the Shapley value.
  arXiv  Detail & Related papers  (2019-02-27T00:22:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.