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Several instance-based explainability methods for finding influential training examples for test-time decisions have been proposed recently, including Influence Functions, TraceIn, Representer Point Selection, Grad-Dot, and Grad-Cos.
Detection of influential observation in linear regression
Cook, R. D. 1977 · 1977
Earlier work this paper cites.
Robust statistics : the approach based on influence functions
Hampel, F. R. 1986 · 1986
Earlier work this paper cites.
A generalized representer theorem
Schölkopf, B.; Herbrich, R.; and Smola, A. J. 2001 · 2001
Earlier work this paper cites.
Influence Functions in Deep Learning Are Fragile
Basu, S.; Pope, P.; and Feizi, S. 2021 · 2006
Earlier work this paper cites.
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey
Das, A.; and Rad, P. 2020 · 2006
Earlier work this paper cites.
MNIST handwritten digit database
LeCun, Y.; Cortes, C.; and Burges, C. 2010 · 2010
Earlier work this paper cites.
Cook’s Distance , 301–302
Cook, R. D. 2011 · 2011
Earlier work this paper cites.
Poisoning Attacks against Support Vector Machines
Biggio, B.; Nelson, B.; and Laskov, P. 2012 · 2012
Earlier work this paper cites.
“Influence sketching”: Finding influential samples in large-scale regressions
Wojnowicz, M.; Cruz, B.; Zhao, X.; Wallace, B.; Wolff, M.; Luan, J.; and Crable, C. 2016 · 2016
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 · 2017
Cited alongside, same era.
Understanding Black-box Predictions via Influence Functions
Koh, P. W.; and Liang, P. 2017 · 2017
Cited alongside, same era.
Representer Point Selection for Explaining Deep Neural Networks
Yeh, C.-K.; Kim, J.; Yen, I. E.-H.; and Ravikumar, P. K. 2018 · 2018
Cited alongside, same era.
Machine Learning Interpretability: A Survey on Methods and Metrics
Carvalho, D. V.; Pereira, E. M.; and Cardoso, J. S. 2019 · 2019
Cited alongside, same era.
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Frankle, J.; and Carbin, M. 2019 · 2019
Later among the works it cites.
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Gu, T.; Dolan-Gavitt, B.; and Garg, S. 2019 · 2019
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Interpretable Machine Learning
Molnar, C. 2019 · 2019
Later among the works it cites.
Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder
Chan, A.; Tay, Y.; Ong, Y.-S.; and Zhang, A. 2020 · 2020
Later among the works it cites.
Auditing Differentially Private Machine Learning: How Private is Private SGD?
Jagielski, M.; Ullman, J.; and Oprea, A. 2020 · 2020
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Influence Functions Do Not Seem to Predict Usefulness in NLP Transfer Learning
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Input Similarity from the Neural Network Perspective
Charpiat, G.; Girard, N.; Felardos, L.; and Tarabalka, Y. 2019 · 2019
Cited alongside, same era.
What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play
Feng, S.; and Boyd-Graber, J. 2019 · 2019
Cited alongside, same era.
Learning to Deceive with Attention-Based Explanations
Pruthi, D.; Gupta, M.; Dhingra, B.; Neubig, G.; and Lipton, Z. C. 2020a
Cited in the paper.
Estimating Training Data Influence by Tracing Gradient Descent
Pruthi, G.; Liu, F.; Kale, S.; and Sundararajan, M. 2020b
Cited in the paper.
CoMNIST - A Dataset of Cyrillic and Latin Hand-written Letters for Machine Learning
Vial, G. ????
Cited in the paper.
Kocijan, V.; and Bowman, S. R. 2020 · 2020
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Evaluation of Similarity-based Explanations
Hanawa, K.; Yokoi, S.; Hara, S.; and Inui, K. 2021 · 2021
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