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We develop a new, principled algorithm for estimating the contribution of training data points to the behavior of a deep learning model, such as a specific prediction it makes.
A value for n-person games
Shapley, L. S · 1953
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Data cleansing: Beyond integrity analysis
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Analysis of regression in game theory approach
Lipovetsky, S. and Conklin, M · 2001
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Compressive sampling
Candès, E. J. et al · 2006
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Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data
Kang, J. D., Schafer, J. L., et al · 2007
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Introduction to the theory of cooperative games , volume 34
Peleg, B. and Sudhölter, P · 2007
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Mostly harmless econometrics: An empiricist’s companion
Angrist, J. D. and Pischke, J.-S · 2008
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Quantitative data cleaning for large databases
Hellerstein, J. M · 2008
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Simultaneous factor selection and collapsing levels in anova
Bondell, H. D. and Reich, B. J · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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The security of machine learning
Barreno, M., Nelson, B., Joseph, A. D., and Tygar, J. D · 2010
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Compressive sensing and structured random matrices
Rauhut, H · 2010
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Factor Selection and Structural Identification in the Interaction ANOVA Model
Post, J. B. and Bondell, H. D · 2012
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Holistic data cleaning: Putting violations into context
Chu, X., Ilyas, I. F., and Papotti, P · 2013
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Causal inference in conjoint analysis: Understanding multidimensional choices via stated preference experiments
Hainmueller, J., Hopkins, D. J., and Yamamoto, T · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Bounding the estimation error of sampling-based shapley value approximation, 2014
Maleki, S., Tran-Thanh, L., Hines, G., Rahwan, T., and Rogers, A · 2014
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Causal inference from 2 k factorial designs by using potential outcomes
Dasgupta, T., Pillai, N. S., and Rubin, D. B · 2015
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Causal inference in statistics, social, and biomedical sciences
Imbens, G. W. and Rubin, D. B · 2015
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Sunlight: Fine-grained targeting detection at scale with statistical confidence
Lecuyer, M., Spahn, R., Spiliopolous, Y., Chaintreau, A., Geambasu, R., and Hsu, D · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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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 · 2016
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Debugging machine learning tasks
Chakarov, A., Nori, A., Rajamani, S., Sen, S., and Vijaykeerthy, D · 2016
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CVXPY: A Python-embedded modeling language for convex optimization
Diamond, S. and Boyd, S · 2016
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An introduction to glmnet, 2016
Hastie, T., Qian, J., and Tay, K · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Auror: Defending against poisoning attacks in collaborative deep learning systems
Shen, S., Tople, S., and Saxena, P · 2016
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Mitigating poisoning attacks on machine learning models: A data provenance based approach
Baracaldo, N., Chen, B., Ludwig, H., and Safavi, J. A · 2017
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
Chen, X., Liu, C., Li, B., Lu, K., and Song, D · 2017
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Emnist: Extending mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and Van Schaik, A · 2017
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Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
Cited alongside, same era.
Boostclean: Automated error detection and repair for machine learning
Krishnan, S., Franklin, M. J., Goldberg, K., and Wu, E · 2017
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Influence functions in deep learning are fragile
Basu, S., Pope, P., and Feizi, S · 2020
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Februus: Input purification defense against trojan attacks on deep neural network systems
Doan, B. G., Abbasnejad, E., and Ranasinghe, D. C · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
Feldman, V. and Zhang, C · 2020
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Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability
Frye, C., Rowat, C., and Feige, I · 2020
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A distributional framework for data valuation
Ghorbani, A., Kim, M., and Zou, J · 2020
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Regularization and the small-ball method ii: complexity dependent error rates
Lecué, G. and Mendelson, S · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Cited alongside, same era.
A rewriting system for convex optimization problems
Agrawal, A., Verschueren, R., Diamond, S., and Boyd, S · 2018
Cited alongside, same era.
Detecting backdoor attacks on deep neural networks by activation clustering
Chen, B., Carvalho, W., Baracaldo, N., Ludwig, H., Edwards, B., Lee, T., Molloy, I., and Srivastava, B · 2018
Cited alongside, same era.
Sentinet: Detecting physical attacks against deep learning systems
Chou, E., Tramèr, F., Pellegrino, G., and Boneh, D · 2018
Cited alongside, same era.
Cleaning crowdsourced labels using oracles for statistical classification
Dolatshah, M., Teoh, M., Wang, J., and Pei, J · 2018
Cited alongside, same era.
Causal interaction in factorial experiments: Application to conjoint analysis
Egami, N. and Imai, K · 2018
Cited alongside, same era.
Lecture notes: Statistics, optimization and algorithms in high dimension, 2020
Pauwels, E · 2020
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Deep k-nn defense against clean-label data poisoning attacks
Peri, N., Gupta, N., Huang, W. R., Fowl, L., Zhu, C., Feizi, S., Goldstein, T., and Dickerson, J. P · 2020
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Estimating training data influence by tracing gradient descent
Pruthi, G., Liu, F., Kale, S., and Sundararajan, M · 2020
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Nnoculation: Broad spectrum and targeted treatment of backdoored dnns
Veldanda, A. K., Liu, K., Tan, B., Krishnamurthy, P., Khorrami, F., Karri, R., Dolan-Gavitt, B., and Garg, S · 2020
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Efficient nonparametric statistical inference on population feature importance using shapley values
Williamson, B. and Feng, J · 2020
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Complaint-driven training data debugging for query 2.0
Wu, W., Flokas, L., Wu, E., and Wang, J · 2020
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wbaek/torchskeleton
Baek, W · 2021
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Improving kernelshap: Practical shapley value estimation using linear regression
Covert, I. and Lee, S.-I · 2021
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Spectre: Defending against backdoor attacks using robust statistics
Hayase, J., Kong, W., Somani, R., and Oh, S · 2021
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Fastshap: Real-time shapley value estimation
Jethani, N., Sudarshan, M., Covert, I., Lee, S.-I., and Ranganath, R · 2021
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Group testing implementation, October 2021
Jia, R · 2021
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kuangliu/pytorch-cifar
Kuangliu · 2021
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pytorch/torchvision/resnet, a
PyTorch · 2021
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pytorch/examples, b
PyTorch · 2021
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Demon in the variant: Statistical analysis of dnns for robust backdoor contamination detection
Tang, D., Wang, X., Tang, H., and Zhang, K · 2021
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bentrevett/pytorch-sentiment-analysis/
Trevett, B · 2021
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Group testing, January 2021
WikipediaContributors · 2021
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Joint shapley values: a measure of joint feature importance
Harris, C., Pymar, R., and Rowat, C · 2022
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Datamodels: Predicting predictions from training data
Ilyas, A., Park, S. M., Engstrom, L., Leclerc, G., and Madry, A · 2022
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Beta shapley: a unified and noise-reduced data valuation framework for machine learning
Kwon, Y. and Zou, J · 2022
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Sampling permutations for shapley value estimation
Mitchell, R., Cooper, J., Frank, E., and Holmes, G · 2022
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