Fetching the paper…
Reading the bibliography…
Even though Shapley value provides an effective explanation for a DNN model prediction, the computation relies on the enumeration of all possible input feature coalitions, which leads to the exponentially growing complexity.
Contributions to the Theory of Games , volume 2
Kuhn, H. W. and Tucker, A. W · 1953
Earlier work this paper cites.
Note on a method for calculating corrected sums of squares and products
Welford, B · 1962
Earlier work this paper cites.
Introduction to the shapley value
Roth, A. E · 1988
Earlier work this paper cites.
The shapley value
Winter, E · 2002
Earlier work this paper cites.
Pearson correlation coefficient
Benesty, J., Chen, J., Huang, Y., and Cohen, I · 2009
Earlier work this paper cites.
Why should i trust you? explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Earlier work this paper cites.
17. A value for n-person games
Shapley, L. S · 2016
Earlier work this paper cites.
European union regulations on algorithmic decision-making and a “right to explanation”
Goodman, B., Flaxman, S., and X, Y · 2017
Earlier work this paper cites.
Deepfm: a factorization-machine based neural network for ctr prediction
Guo, H., Tang, R., Ye, Y., Li, Z., and He, X · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Earlier work this paper cites.
L-shapley and c-shapley: Efficient model interpretation for structured data
Chen, J., Song, L., Wainwright, M. J., and Jordan, M. I · 2018
Cited alongside, same era.
Plaster: A framework for deep learning performance
Teich, D. A. and Teich, P. R · 2018
Cited alongside, same era.
Towards interpretation of recommender systems with sorted explanation paths
Yang, F., Liu, N., Wang, S., and Hu, X · 2018
Cited alongside, same era.
Explaining deep neural networks with a polynomial time algorithm for shapley value approximation
Ancona, M., Oztireli, C., and Gross, M · 2019
Cited alongside, same era.
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., and Dean, J · 2019
Cited alongside, same era.
Establishing the rules for building trustworthy ai
Captum: A unified and generic model interpretability library for pytorch
Kokhlikyan, N., Miglani, V., Martin, M., Wang, E., Alsallakh, B., Reynolds, J., Melnikov, A., Kliushkina, N., Araya, C., Yan, S., et al · 2020
Later among the works it cites.
When causal inference meets deep learning
Luo, Y., Peng, J., and Ma, J · 2020
Later among the works it cites.
Feature importance ranking for deep learning
Wojtas, M. and Chen, K · 2020
Later among the works it cites.
Improving kernelshap: Practical shapley value estimation using linear regression
Covert, I. and Lee, S.-I · 2021
Later among the works it cites.
Synthetic benchmarks for scientific research in explainable machine learning
Liu, Y., Khandagale, S., White, C., and Neiswanger, W · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Floridi, L · 2019
Cited alongside, same era.
Towards efficient data valuation based on the shapley value
Jia, R., Dao, D., Wang, B., Hubis, F. A., Hynes, N., Gürel, N. M., Li, B., Zhang, C., Song, D., and Spanos, C. J · 2019
Cited alongside, same era.
Antithetic and monte carlo kernel estimators for partial rankings
Lomeli, M., Rowland, M., Gretton, A., and G., Z · 2019
Cited alongside, same era.
Generating contrastive explanations with monotonic attribute functions
Luss, R., Chen, P.-Y., Dhurandhar, A., Sattigeri, P., Zhang, Y., Shanmugam, K., and Tu, C.-C · 2019
Cited alongside, same era.
Intelligible and explainable machine learning: Best practices and practical challenges
Caruana, R., Lundberg, S., Ribeiro, M. T., Nori, H., and Jenkins, S · 2020
Cited alongside, same era.
Understanding global feature contributions with additive importance measures
Covert, I., Lundberg, S. M., and Lee, S.-I · 2020
Cited alongside, same era.
Mitchell, R., Cooper, J., Frank, E., and Holmes, G · 2021
Later among the works it cites.
On the tractability of shap explanations
Van D. B., G., Lykov, A., S., M., and S., D · 2021
Later among the works it cites.
Wang, R., Wang, X., and Inouye, D. I · 2021
Later among the works it cites.
Model-based counterfactual synthesizer for interpretation
Yang, F., Alva, S. S., Chen, J., and Hu, X · 2021
Later among the works it cites.
Efficient xai techniques: A taxonomic survey
Chuang, Y.-N., Wang, G., Yang, F., Liu, Z., Cai, X., Du, M., and Hu, X · 2023
Closest in time.