Fetching the paper…
Reading the bibliography…
A learning algorithm $A$ trained on a dataset $D$ is revealed to have poor performance on some subpopulation at test time.
Towards efficient data valuation based on the shapley value
Jia, R., Dao, D., Wang, B., Hubis, F. A., Hynes, N., Gurel, N. M., Li, B., Zhang, C., Song, D., and Spanos, C. (2019) · 1902
Earlier work this paper cites.
What do compressed deep neural networks forget?
Hooker, S., Courville, A., Clark, G., Dauphin, Y., and Frome, A. (2019) · 1911
Earlier work this paper cites.
A value for n-person games
Shapley, L. S. (1953) · 1953
Earlier work this paper cites.
Feature selection via coalitional game theory
Cohen, S., Dror, G., and Ruppin, E. (2007) · 1961
Earlier work this paper cites.
Values of large games ii: Oceanic games
Milnor, J. W. and Shapley, L. S. (1978) · 1978
Earlier work this paper cites.
The Shapley value: essays in honor of Lloyd S. Shapley
Shapley, L. S., Roth, A. E., et al. (1988) · 1988
Earlier work this paper cites.
Bargaining foundations of shapley value
Gul, F. (1989) · 1989
Earlier work this paper cites.
Effective unconstrained face recognition by combining multiple descriptors and learned background statistics
Wolf, L., Hassner, T., and Taigman, Y. (2011) · 1990
Earlier work this paper cites.
A distributional framework for data valuation
Ghorbani, A., Kim, M. P., and Zou, J. (2020) · 2002
Earlier work this paper cites.
A linear approximation method for the shapley value
Fatima, S. S., Wooldridge, M., and Jennings, N. R. (2008) · 2008
Earlier work this paper cites.
Polynomial calculation of the shapley value based on sampling
Castro, J., Gómez, D., and Tejada, J. (2009) · 2009
Earlier work this paper cites.
Characterising bias in compressed models
Hooker, S., Moorosi, N., Clark, G., Bengio, S., and Denton, E. (2020) · 2010
Earlier work this paper cites.
An efficient explanation of individual classifications using game theory
Kononenko, I. et al. (2010) · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011) · 2011
Earlier work this paper cites.
Bounding the estimation error of sampling-based shapley value approximation
Maleki, S., Tran-Thanh, L., Hines, G., Rahwan, T., and Rogers, A. (2013) · 2013
Cited alongside, same era.
Efficient computation of the shapley value for game-theoretic network centrality
Michalak, T. P., Aadithya, K. V., Szczepanski, P. L., Ravindran, B., and Jennings, N. R. (2013) · 2013
Cited alongside, same era.
Sobol’indices and shapley value
Owen, A. B. (2014) · 2014
Cited alongside, same era.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
Cited alongside, same era.
Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · 2015
Cited alongside, same era.
L-shapley and c-shapley: Efficient model interpretation for structured data
Chen, J., Song, L., Wainwright, M. J., and Jordan, M. I. (2018) · 2018
Later among the works it cites.
Quantifying algorithmic improvements over time
Kotthoff, L., Fréchette, A., Michalak, T. P., Rahwan, T., Hoos, H. H., and Leyton-Brown, K. (2018) · 2018
Later among the works it cites.
Consistent individualized feature attribution for tree ensembles
Lundberg, S. M., Erion, G. G., and Lee, S.-I. (2018) · 2018
Later among the works it cites.
Ai can be sexist and racist—it’s time to make it fair
Zou, J. and Schiebinger, L. (2018) · 2018
Later among the works it cites.
Differential privacy has disparate impact on model accuracy
Bagdasaryan, E., Poursaeed, O., and Shmatikov, V. (2019) · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Datta, A., Sen, S., and Zick, Y. (2016) · 2016
Cited alongside, same era.
Using the shapley value to analyze algorithm portfolios
Fréchette, A., Kotthoff, L., Michalak, T., Rahwan, T., Hoos, H., and Leyton-Brown, K. (2016) · 2016
Cited alongside, same era.
A new approximation method for the shapley value applied to the wtc 9/11 terrorist attack
Hamers, H., Husslage, B., Lindelauf, R., Campen, T., et al. (2016) · 2016
Cited alongside, same era.
UCI machine learning repository
Dua, D. and Graff, C. (2017) · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I. (2017) · 2017
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A. (2017) · 2017
Cited alongside, same era.
A marketplace for data: an algorithmic solution
Agarwal, A., Dahleh, M., and Sarkar, T. (2018) · 2018
Cited alongside, same era.
Interpretation of neural networks is fragile
Ghorbani, A., Abid, A., and Zou, J. (2019) · 2019
Closest in time.
Data shapley: Equitable valuation of data for machine learning
Ghorbani, A. and Zou, J. (2019) · 2019
Closest in time.
Multiaccuracy: Black-box post-processing for fairness in classification
Kim, M. P., Ghorbani, A., and Zou, J. (2019) · 2019
Closest in time.
Lessons from the pulse model and discussion
Gradient, T. (June 24, 2020) · 2020
Closest in time.
Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Menon, S., Damian, A., Hu, S., Ravi, N., and Rudin, C. (2020) · 2020
Closest in time.
Ai weekly: A deep learning pioneer’s teachable moment on ai bias
Venturebeat (June 26, 2020) · 2020
Closest in time.
If you like shapley then you’ll love the core
Yan, T. and Procaccia, A. (2020) · 2020
Closest in time.
Moving beyond “algorithmic bias is a data problem”
Hooker, S. (2021) · 2021
Closest in time.