Efficient Shapley Values Estimation by Amortization for Text Classification

• URL: http://arxiv.org/abs/2305.19998v1
• Date: Wed, 31 May 2023 16:19:13 GMT
• Title: Efficient Shapley Values Estimation by Amortization for Text Classification
• Authors: Chenghao Yang, Fan Yin, He He, Kai-Wei Chang, Xiaofei Ma, Bing Xiang
• Abstract summary: 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.
• Score: 66.7725354593271
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the popularity of Shapley Values in explaining neural text classification models, computing them is prohibitive for large pretrained models due to a large number of model evaluations. In practice, Shapley Values are often estimated with a small number of stochastic model evaluations. However, we show that the estimated Shapley Values are sensitive to random seed choices -- the top-ranked features often have little overlap across different seeds, especially on examples with longer input texts. This can only be mitigated by aggregating thousands of model evaluations, which on the other hand, induces substantial computational overheads. To mitigate the trade-off between stability and efficiency, we develop an amortized model that directly predicts each input feature's Shapley Value without additional model evaluations. It is trained on a set of examples whose Shapley Values are estimated from a large number of model evaluations to ensure stability. Experimental results on two text classification datasets demonstrate that our amortized model estimates Shapley Values accurately with up to 60 times speedup compared to traditional methods. Furthermore, the estimated values are stable as the inference is deterministic. We release our code at https://github.com/yangalan123/Amortized-Interpretability.

Related papers

• Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions [38.87540833773233]
  We propose a notion of robustness on the sign of the instance score. We introduce an efficient fine-tuning-free approximation of the Shapley value for instance attribution.
  arXiv  Detail & Related papers  (2024-06-07T03:29:57Z)
• Accelerated Shapley Value Approximation for Data Evaluation [3.707457963532597]
  We show that Shapley value of data points can be approximated more efficiently by leveraging structural properties of machine learning problems. Our analysis suggests that in fact models trained on small subsets are more important in context of data valuation.
  arXiv  Detail & Related papers  (2023-11-09T13:15:36Z)
• 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)
• An Efficient Shapley Value Computation for the Naive Bayes Classifier [0.0]
  This article proposes an exact analytic expression of Shapley values in the case of the naive Bayes classifier. Results show that our Shapley proposal for the naive Bayes provides informative results with low algorithmic complexity.
  arXiv  Detail & Related papers  (2023-07-31T14:39:10Z)
• 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)
• Exact Shapley Values for Local and Model-True Explanations of Decision Tree Ensembles [0.0]
  We consider the application of Shapley values for explaining decision tree ensembles. We present a novel approach to Shapley value-based feature attribution that can be applied to random forests and boosted decision trees.
  arXiv  Detail & Related papers  (2021-12-16T20:16:02Z)
• Robust Implicit Networks via Non-Euclidean Contractions [63.91638306025768]
  Implicit neural networks show improved accuracy and significant reduction in memory consumption. They can suffer from ill-posedness and convergence instability. This paper provides a new framework to design well-posed and robust implicit neural networks.
  arXiv  Detail & Related papers  (2021-06-06T18:05:02Z)
• Flexible Model Aggregation for Quantile Regression [92.63075261170302]
  Quantile regression is a fundamental problem in statistical learning motivated by a need to quantify uncertainty in predictions. We investigate methods for aggregating any number of conditional quantile models. All of the models we consider in this paper can be fit using modern deep learning toolkits.
  arXiv  Detail & Related papers  (2021-02-26T23:21:16Z)
• Predictive and Causal Implications of using Shapley Value for Model Interpretation [6.744385328015561]
  We established the relationship between Shapley value and conditional independence, a key concept in both predictive and causal modeling. Our results indicate that, eliminating a variable with high Shapley value from a model do not necessarily impair predictive performance. More importantly, Shapley value of a variable do not reflect their causal relationship with the target of interest.
  arXiv  Detail & Related papers  (2020-08-12T01:08:08Z)
• 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.