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Influence functions estimate effect of individual data points on predictions of the model on test data and were adapted to deep learning in Koh and Liang [2017].
The infinitesimal jackknife
Louis A Jaeckel · 1972
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The influence curve and its role in robust estimation
Frank R Hampel · 1974
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Detection of influential observation in linear regression
R Dennis Cook · 1977
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Testing halfspaces
Kevin Matulef, Ryan O’Donnell, Ronitt Rubinfeld, and Rocco A Servedio · 2010
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Analysis of boolean functions
Ryan O’Donnell · 2014
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Learnability of influence in networks
Harikrishna Narasimhan, David C Parkes, and Yaron Singer · 2015
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Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Earlier work this paper cites.
Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Representer point selection for explaining deep neural networks
Chih-Kuan Yeh, Joon Kim, Ian En-Hsu Yen, and Pradeep K Ravikumar · 2018
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Understanding the origins of bias in word embeddings
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, and Richard Zemel · 2019
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Input similarity from the neural network perspective
Guillaume Charpiat, Nicolas Girard, Loris Felardos, and Yuliya Tarabalka · 2019
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Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
Cited alongside, same era.
Towards efficient data valuation based on the shapley value
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, and Costas J Spanos · 2019
Cited alongside, same era.
Interpreting black box predictions using fisher kernels
Rajiv Khanna, Been Kim, Joydeep Ghosh, and Sanmi Koyejo · 2019
Cited alongside, same era.
On the accuracy of influence functions for measuring group effects
Pang Wei W Koh, Kai-Siang Ang, Hubert Teo, and Percy S Liang · 2019
Cited alongside, same era.
On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
Cited alongside, same era.
Can you trust this prediction? auditing pointwise reliability after learning
Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
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Evaluation of similarity-based explanations
Kazuaki Hanawa, Sho Yokoi, Satoshi Hara, and Kentaro Inui · 2020
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Estimating training data influence by tracing gradient descent
Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan · 2020
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Fastif: Scalable influence functions for efficient model interpretation and debugging
Han Guo, Nazneen Rajani, Peter Hase, Mohit Bansal, and Caiming Xiong · 2021
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Peter Schulam and Suchi Saria · 2019
Cited alongside, same era.
Repairing without retraining: Avoiding disparate impact with counterfactual distributions
Hao Wang, Berk Ustun, and Flavio Calmon · 2019
Cited alongside, same era.
Effects of influence on user trust in predictive decision making
Jianlong Zhou, Zhidong Li, Huaiwen Hu, Kun Yu, Fang Chen, Zelin Li, and Yang Wang · 2019
Cited alongside, same era.
Discriminative jackknife: Quantifying uncertainty in deep learning via higher-order influence functions
Ahmed Alaa and Mihaela Van Der Schaar · 2020
Cited alongside, same era.
Relatif: Identifying explanatory training samples via relative influence
Elnaz Barshan, Marc-Etienne Brunet, and Gintare Karolina Dziugaite · 2020
Cited alongside, same era.
Multi-stage influence function
Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane Boning, and Cho-Jui Hsieh · 2020
Cited alongside, same era.
Detecting adversarial samples using influence functions and nearest neighbors
Gilad Cohen, Guillermo Sapiro, and Raja Giryes · 2020
Cited alongside, same era.
Shuming Kong, Yanyan Shen, and Linpeng Huang · 2021
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Interactive label cleaning with example-based explanations
Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, and Andrea Passerini · 2021
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If influence functions are the answer, then what is the question?, 2022
Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, and Roger Grosse · 2022
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Datamodels: Predicting predictions from training data
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, and Aleksander Madry · 2022
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Scaling up influence functions
Andrea Schioppa, Polina Zablotskaia, David Vilar, and Artem Sokolov · 2022
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Rethinking influence functions of neural networks in the over-parameterized regime
Rui Zhang and Shihua Zhang · 2022
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