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One of the most effective approaches to improving the performance of a machine learning model is to procure additional training data.
A value for n-person games
Shapley, L. S. (1952) · 1952
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A value for n-person games
Shapley, L. S. (1952) · 1952
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The influence curve and its role in robust estimation
Hampel, F. R. (1974) · 1974
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The influence curve and its role in robust estimation
Hampel, F. R. (1974) · 1974
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Detection of influential observation in linear regression
Cook, R. D. (1977) · 1977
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Detection of influential observation in linear regression
Cook, R. D. (1977) · 1977
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Residuals and influence in regression
Cook, R. D. and Weisberg, S. (1982) · 1982
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Residuals and influence in regression
Cook, R. D. and Weisberg, S. (1982) · 1982
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On the limited memory bfgs method for large scale optimization
Liu, D. C. and Nocedal, J. (1989) · 1989
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On the limited memory bfgs method for large scale optimization
Liu, D. C. and Nocedal, J. (1989) · 1989
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The MNIST database of handwritten digits
LeCun, Y. and Cortes, C. (1998) · 1998
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The MNIST database of handwritten digits
LeCun, Y. and Cortes, C. (1998) · 1998
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Computing the relative value of spatio-temporal data in wholesale and retail data marketplaces
Azcoitia, S. A., Paraschiv, M., and Laoutaris, N. (2020) · 2002
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Computing the relative value of spatio-temporal data in wholesale and retail data marketplaces
Azcoitia, S. A., Paraschiv, M., and Laoutaris, N. (2020) · 2002
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Influence functions in deep learning are fragile
Basu, S., Pope, P., and Feizi, S. (2020a) · 2006
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Influence functions in deep learning are fragile
Basu, S., Pope, P., and Feizi, S. (2020a) · 2006
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Ibm federated learning: an enterprise framework white paper v0. 1
Ludwig, H., Baracaldo, N., Thomas, G., Zhou, Y., Anwar, A., Rajamoni, S., Ong, Y., Radhakrishnan, J., Verma, A., Sinn, M., et al. (2020) · 2007
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Ibm federated learning: an enterprise framework white paper v0. 1
Ludwig, H., Baracaldo, N., Thomas, G., Zhou, Y., Anwar, A., Rajamoni, S., Ong, Y., Radhakrishnan, J., Verma, A., Sinn, M., et al. (2020) · 2007
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Spearman Rank Correlation Coefficient
Dodge, Y. (2008) · 2008
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Beyond convexity: Submodularity in machine learning
Krause, A. and Guestrin, C. (2008) · 2008
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A utility-theoretic approach to privacy and personalization
Krause, A. and Horvitz, E. (2008) · 2008
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Spearman Rank Correlation Coefficient
Dodge, Y. (2008) · 2008
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Beyond convexity: Submodularity in machine learning
Krause, A. and Guestrin, C. (2008) · 2008
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A utility-theoretic approach to privacy and personalization
Krause, A. and Horvitz, E. (2008) · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. (2009) · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. (2009) · 2009
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Model-specific data subsampling with influence functions
Raj, A., Musco, C., Mackey, L., and Fusi, N. (2020) · 2010
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Model-specific data subsampling with influence functions
Raj, A., Musco, C., Mackey, L., and Fusi, N. (2020) · 2010
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Try before you buy: A practical data purchasing algorithm for real-world data marketplaces
Azcoitia, S. A. and Laoutaris, N. (2020) · 2012
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Fastif: Scalable influence functions for efficient model interpretation and debugging
Guo, H., Rajani, N. F., Hase, P., Bansal, M., and Xiong, C. (2020b) · 2012
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Try before you buy: A practical data purchasing algorithm for real-world data marketplaces
Azcoitia, S. A. and Laoutaris, N. (2020) · 2012
Cited alongside, same era.
Fastif: Scalable influence functions for efficient model interpretation and debugging
Guo, H., Rajani, N. F., Hase, P., Bansal, M., and Xiong, C. (2020b) · 2012
Cited alongside, same era.
A principled approach to data valuation for federated learning
Wang, T., Rausch, J., Zhang, C., Jia, R., and Song, D. (2020) · 2012
Cited alongside, same era.
Submodular function maximization
Optimal subsampling with influence functions
Ting, D. and Brochu, E. (2018) · 2018
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Data shapley: Equitable valuation of data for machine learning
Ghorbani, A. and Zou, J. (2019) · 2019
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A swiss army infinitesimal jackknife
Giordano, R., Stephenson, W., Liu, R., Jordan, M., and Broderick, T. (2019) · 2019
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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) · 2019
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On the accuracy of influence functions for measuring group effects
Koh, P. W. W., Ang, K.-S., Teo, H., and Liang, P. S. (2019) · 2019
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Deep learning to improve breast cancer detection on screening mammography
Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., and Sieh, W. (2019) · 2019
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Krause, A. and Golovin, D. (2014) · 2014
Cited alongside, same era.
A theory of pricing private data
Li, C., Li, D. Y., Miklau, G., and Suciu, D. (2014) · 2014
Cited alongside, same era.
Submodular function maximization
Krause, A. and Golovin, D. (2014) · 2014
Cited alongside, same era.
A theory of pricing private data
Li, C., Li, D. Y., Miklau, G., and Suciu, D. (2014) · 2014
Cited alongside, same era.
Deep speech 2: End-to-end speech recognition in english and mandarin
Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., Chen, G., et al. (2016) · 2016
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Oblivious multi-party machine learning on trusted processors
Ohrimenko, O., Schuster, F., Fournet, C., Mehta, A., Nowozin, S., Vaswani, K., and Costa, M. (2016) · 2016
Cited alongside, same era.
Deep speech 2: End-to-end speech recognition in english and mandarin
Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., Chen, G., et al. (2016) · 2016
Cited alongside, same era.
Oblivious multi-party machine learning on trusted processors
Ohrimenko, O., Schuster, F., Fournet, C., Mehta, A., Nowozin, S., Vaswani, K., and Costa, M. (2016) · 2016
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Later among the works it cites.
Profit allocation for federated learning
Song, T., Tong, Y., and Wei, S. (2019) · 2019
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Measure contribution of participants in federated learning
Wang, G., Dang, C. X., and Zhou, Z. (2019) · 2019
Later among the works it cites.
Data shapley: Equitable valuation of data for machine learning
Ghorbani, A. and Zou, J. (2019) · 2019
Later among the works it cites.
A swiss army infinitesimal jackknife
Giordano, R., Stephenson, W., Liu, R., Jordan, M., and Broderick, T. (2019) · 2019
Later among the works it cites.
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) · 2019
Later among the works it cites.
On the accuracy of influence functions for measuring group effects
Koh, P. W. W., Ang, K.-S., Teo, H., and Liang, P. S. (2019) · 2019
Later among the works it cites.
Deep learning to improve breast cancer detection on screening mammography
Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., and Sieh, W. (2019) · 2019
Later among the works it cites.
Profit allocation for federated learning
Song, T., Tong, Y., and Wei, S. (2019) · 2019
Later among the works it cites.
Measure contribution of participants in federated learning
Wang, G., Dang, C. X., and Zhou, Z. (2019) · 2019
Later among the works it cites.
Common voice: A massively-multilingual speech corpus
Ardila, R., Branson, M., Davis, K., Kohler, M., Meyer, J., Henretty, M., Morais, R., Saunders, L., Tyers, F., and Weber, G. (2020) · 2020
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CrypTen (v 0.1)
CrypTen (2020) · 2020
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Crypten: Secure multi-party computation meets machine learning
Knott, B., Venkataraman, S., Hannun, A., Sengupta, S., Ibrahim, M., and van der Maaten, L. (2020) · 2020
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Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation
Majkowska, A., Mittal, S., Steiner, D. F., Reicher, J. J., McKinney, S. M., Duggan, G. E., Eswaran, K., Cameron Chen, P.-H., Liu, Y., Kalidindi, S. R., et al. (2020) · 2020
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Microsoft SEAL (release 3.5)
SEAL (2020) · 2020
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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) · 2020
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Common voice: A massively-multilingual speech corpus
Ardila, R., Branson, M., Davis, K., Kohler, M., Meyer, J., Henretty, M., Morais, R., Saunders, L., Tyers, F., and Weber, G. (2020) · 2020
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CrypTen (v 0.1)
CrypTen (2020) · 2020
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Crypten: Secure multi-party computation meets machine learning
Knott, B., Venkataraman, S., Hannun, A., Sengupta, S., Ibrahim, M., and van der Maaten, L. (2020) · 2020
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Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation
Majkowska, A., Mittal, S., Steiner, D. F., Reicher, J. J., McKinney, S. M., Duggan, G. E., Eswaran, K., Cameron Chen, P.-H., Liu, Y., Kalidindi, S. R., et al. (2020) · 2020
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Microsoft SEAL (release 3.5)
SEAL (2020) · 2020
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Subpopulation data poisoning attacks
Jagielski, M., Severi, G., Pousette Harger, N., and Oprea, A. (2021) · 2021
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Subpopulation data poisoning attacks
Jagielski, M., Severi, G., Pousette Harger, N., and Oprea, A. (2021) · 2021
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