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Data valuation is a powerful framework for providing statistical insights into which data are beneficial or detrimental to model training.
A value for n-person games
Shapley, L. S · 1953
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The infinitesimal jackknife. memorandum
Jaeckel, L · 1972
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On some topics in robustness
Mallows, C. L · 1975
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Bootstrap methods: Another look at the jackknife
Efron, B · 1979
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Characterizations of an empirical influence function for detecting influential cases in regression
Cook, R. D. and Weisberg, S · 1980
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Residuals and influence in regression
Cook, R. D. and Weisberg, S · 1982
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Jackknife-after-bootstrap standard errors and influence functions
Efron, B · 1992
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Cross-validation and the bootstrap: Estimating the error rate of a prediction rule
Efron, B. and Tibshirani, R. J · 1995
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Bayesian data analysis
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B · 1995
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Bagging predictors
Breiman, L · 1996
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Improvements on cross-validation: the 632+ bootstrap method
Efron, B. and Tibshirani, R · 1997
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Random forests
Breiman, L · 2001
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k-means++ the advantages of careful seeding
Arthur, D. and Vassilvitskii, S · 2007
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Approximating power indices: theoretical and empirical analysis
Bachrach, Y., Markakis, E., Resnick, E., Procaccia, A. D., Rosenschein, J. S., and Saberi, A · 2010
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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
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Bounding the estimation error of sampling-based shapley value approximation
Maleki, S., Tran-Thanh, L., Hines, G., Rahwan, T., and Rogers, A · 2013
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Confidence intervals for random forests: The jackknife and the infinitesimal jackknife
Wager, S., Hastie, T., and Efron, B · 2014
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Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
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Panning for gold:‘model-x’knockoffs for high dimensional controlled variable selection
Candes, E., Fan, Y., Janson, L., and Lv, J · 2018
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Analysing neural network topologies: a game theoretic approach
Stier, J., Gianini, G., Granitzer, M., and Ziegler, K · 2018
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A marketplace for data: An algorithmic solution
Agarwal, A., Dahleh, M., and Sarkar, T · 2019
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A principled approach to data valuation for federated learning
Wang, T., Rausch, J., Zhang, C., Jia, R., and Song, D · 2020
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Data valuation using reinforcement learning
Yoon, J., Arik, S., and Pfister, T · 2020
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Explaining by removing: A unified framework for model explanation
Covert, I., Lundberg, S., and Lee, S.-I · 2021
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Openml-python: an extensible python api for openml
Feurer, M., van Rijn, J. N., Kadra, A., Gijsbers, P., Mallik, N., Ravi, S., Muller, A., Vanschoren, J., and Hutter, F · 2021
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Efficient computation and analysis of distributional shapley values
Kwon, Y., Rivas, M. A., and Zou, J · 2021
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Data valuation for medical imaging using shapley value and application to a large-scale chest x-ray dataset
Tang, S., Ghorbani, A., Yamashita, R., Rehman, S., Dunnmon, J. A., Zou, J., and Rubin, D. L · 2021
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Generalized random forests
Athey, S., Tibshirani, J., and Wager, S · 2019
Cited alongside, same era.
Data shapley: Equitable valuation of data for machine learning
Ghorbani, A. and Zou, J · 2019
Cited alongside, same era.
Important complexity reduction of random forest in multi-classification problem
Hassine, K., Erbad, A., and Hamila, R · 2019
Cited alongside, same era.
Interpret federated learning with shapley values
Wang, G · 2019
Cited alongside, same era.
Influence functions in deep learning are fragile
Basu, S., Pope, P., and Feizi, S · 2020
Cited alongside, same era.
What neural networks memorize and why: Discovering the long tail via influence estimation
Feldman, V. and Zhang, C · 2020
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Gradient driven rewards to guarantee fairness in collaborative machine learning
Xu, X., Lyu, L., Ma, X., Miao, C., Foo, C. S., and Low, B. K. H · 2021
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If you like shapley then you’ll love the core
Yan, T. and Procaccia, A. D · 2021
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Datamodels: Understanding predictions with data and data with predictions
Ilyas, A., Park, S. M., Engstrom, L., Leclerc, G., and Madry, A · 2022
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Measuring the effect of training data on deep learning predictions via randomized experiments
Lin, J., Zhang, A., Lécuyer, M., Li, J., Panda, A., and Sen, S · 2022
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The shapley value in machine learning
Rozemberczki, B., Watson, L., Bayer, P., Yang, H.-T., Kiss, O., Nilsson, S., and Sarkar, R · 2022
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CS-shapley: Class-wise shapley values for data valuation in classification
Schoch, S., Xu, H., and Ji, Y · 2022
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Data valuation in machine learning:“ingredients”, strategies, and open challenges
Sim, R. H. L., Xu, X., and Low, B. K. H · 2022
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Private data valuation and fair payment in data marketplaces
Tian, Z., Liu, J., Li, J., Cao, X., Jia, R., and Ren, K · 2022
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Data banzhaf: A data valuation framework with maximal robustness to learning stochasticity
Wang, T. and Jia, R · 2022
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Robust data valuation via variance reduced data shapley
Wu, M., Jia, R., Huang, W., Chang, X., et al · 2022
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