Shapley Value Approximation Based on k-Additive Games
- URL: http://arxiv.org/abs/2502.04763v1
- Date: Fri, 07 Feb 2025 08:52:57 GMT
- Title: Shapley Value Approximation Based on k-Additive Games
- Authors: Guilherme Dean Pelegrina, Patrick Kolpaczki, Eyke Hüllermeier,
- Abstract summary: The Shapley value is the prevalent solution for fair division problems in which a payout is to be divided among multiple agents.
Despite its popularity and axiomatic justification, the Shapley value suffers from a computational complexity that scales exponentially with the number of entities involved.
We propose SVA$k_textADD$, a novel approximation method that fits a $k$-additive surrogate game.
- Score: 21.023521613326654
- License:
- Abstract: The Shapley value is the prevalent solution for fair division problems in which a payout is to be divided among multiple agents. By adopting a game-theoretic view, the idea of fair division and the Shapley value can also be used in machine learning to quantify the individual contribution of features or data points to the performance of a predictive model. Despite its popularity and axiomatic justification, the Shapley value suffers from a computational complexity that scales exponentially with the number of entities involved, and hence requires approximation methods for its reliable estimation. We propose SVA$k_{\text{ADD}}$, a novel approximation method that fits a $k$-additive surrogate game. By taking advantage of $k$-additivity, we are able to elicit the exact Shapley values of the surrogate game and then use these values as estimates for the original fair division problem. The efficacy of our method is evaluated empirically and compared to competing methods.
Related papers
- 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) - DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation [23.646508094051768]
We consider the dataset valuation problem, that is, the problem of quantifying the incremental gain.
The Shapley value is a natural tool to perform dataset valuation due to its formal axiomatic justification.
We propose a novel approximation, referred to as discrete uniform Shapley, which is expressed as an expectation under a discrete uniform distribution.
arXiv Detail & Related papers (2023-06-03T10:22:50Z) - 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) - Approximating the Shapley Value without Marginal Contributions [11.539320505465149]
The Shapley value, which is arguably the most popular approach for assigning a meaningful contribution value to players in a cooperative game, has recently been used intensively in explainable artificial intelligence.
We propose two parameter-free and domain-independent approximation algorithms based on a representation of the Shapley value detached from the notion of marginal contribution.
arXiv Detail & Related papers (2023-02-01T20:14:22Z) - A $k$-additive Choquet integral-based approach to approximate the SHAP
values for local interpretability in machine learning [8.637110868126546]
This paper aims at providing some interpretability for machine learning models based on Shapley values.
A SHAP-based method called Kernel SHAP adopts an efficient strategy that approximates such values with less computational effort.
The obtained results attest that our proposal needs less computations on coalitions of attributes to approximate the SHAP values.
arXiv Detail & Related papers (2022-11-03T22:34:50Z) - Direct Advantage Estimation [63.52264764099532]
We show that the expected return may depend on the policy in an undesirable way which could slow down learning.
We propose the Direct Advantage Estimation (DAE), a novel method that can model the advantage function and estimate it directly from data.
If desired, value functions can also be seamlessly integrated into DAE and be updated in a similar way to Temporal Difference Learning.
arXiv Detail & Related papers (2021-09-13T16:09:31Z) - Scalable Personalised Item Ranking through Parametric Density Estimation [53.44830012414444]
Learning from implicit feedback is challenging because of the difficult nature of the one-class problem.
Most conventional methods use a pairwise ranking approach and negative samplers to cope with the one-class problem.
We propose a learning-to-rank approach, which achieves convergence speed comparable to the pointwise counterpart.
arXiv Detail & Related papers (2021-05-11T03:38:16Z) - $\gamma$-ABC: Outlier-Robust Approximate Bayesian Computation Based on a
Robust Divergence Estimator [95.71091446753414]
We propose to use a nearest-neighbor-based $gamma$-divergence estimator as a data discrepancy measure.
Our method achieves significantly higher robustness than existing discrepancy measures.
arXiv Detail & Related papers (2020-06-13T06:09:27Z) - 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.