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We consider the problem of identifying a minimal subset of training data $\mathcal{S}_t$ such that if the instances comprising $\mathcal{S}_t$ had been removed prior to training, the categorization of a given test point $x_t$ would have been different.
Relatif: Identifying explanatory training samples via relative influence
Elnaz Barshan, Marc-Etienne Brunet, and Gintare Karolina Dziugaite. 2020 · 1909
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Learning the difference that makes a difference with counterfactually-augmented data
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Combining feature and instance attribution to detect artifacts
Pouya Pezeshkpour, Sarthak Jain, Sameer Singh, and Byron Wallace. 2022 · 1946
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The influence curve and its role in robust estimation
Frank R Hampel. 1974 · 1974
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Characterizations of an empirical influence function for detecting influential cases in regression
R Dennis Cook and Sanford Weisberg. 1980 · 1980
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Residuals and influence in regression
R Dennis Cook and Sanford Weisberg. 1982 · 1982
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The debate on automated essay grading
Marti A Hearst. 2000 · 2000
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Explaining black box predictions and unveiling data artifacts through influence functions
Xiaochuang Han, Byron C Wallace, and Yulia Tsvetkov. 2020 · 2005
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Twitter sentiment classification using distant supervision
Alec Go, Richa Bhayani, and Lei Huang. 2009 · 2009
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The hewlett foundation: Automated essay scoring
Hewlett Foundation. 2010 · 2010
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Debugging tests for model explanations
Julius Adebayo, Michael Muelly, Ilaria Liccardi, and Been Kim. 2020 · 2011
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An automatic finite-sample robustness metric: When can dropping a little data make a big difference?
Tamara Broderick, Ryan Giordano, and Rachael Meager. 2020 · 2011
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
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Designing contestability: Interaction design, machine learning, and mental health
Tad Hirsch, Kritzia Merced, Shrikanth Narayanan, Zac E Imel, and David C Atkins. 2017 · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 2017 · 2017
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Input similarity from the neural network perspective
Guillaume Charpiat, Nicolas Girard, Loris Felardos, and Yuliya Tarabalka. 2019 · 2019
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Interpreting black box predictions using fisher kernels
Rajiv Khanna, Been Kim, Joydeep Ghosh, and Sanmi Koyejo. 2019 · 2019
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On the accuracy of influence functions for measuring group effects
Pang Wei W Koh, Kai-Siang Ang, Hubert Teo, and Percy S Liang. 2019 · 2019
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Disentangling influence: Using disentangled representations to audit model predictions
Charles Marx, Richard Phillips, Sorelle Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2019 · 2019
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Can you trust this prediction? auditing pointwise reliability after learning
Peter Schulam and Suchi Saria. 2019 · 2019
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Hate Speech Dataset from a White Supremacy Forum
Ona de Gibert, Naiara Perez, Aitor García-Pablos, and Montse Cuadros. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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CARER: Contextualized affect representations for emotion recognition
Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen. 2018 · 2018
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Optimal subsampling with influence functions
Daniel Ting and Eric Brochu. 2018 · 2018
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Human intervention in automated decision-making: Toward the construction of contestable systems
Marco Almada. 2019 · 2019
Cited alongside, same era.
Kristen Vaccaro, Karrie Karahalios, Deirdre K Mulligan, Daniel Kluttz, and Tad Hirsch. 2019 · 2019
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Influence tuning: Demoting spurious correlations via instance attribution and instance-driven updates
Xiaochuang Han and Yulia Tsvetkov. 2021 · 2021
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Interactive label cleaning with example-based explanations
Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, and Andrea Passerini. 2021 · 2021
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Minimal explanations for neural network predictions
Ouns El Harzli, Bernardo Cuenca Grau, and Ian Horrocks. 2022 · 2022
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Datamodels: Understanding predictions with data and data with predictions
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, and Aleksander Madry. 2022 · 2022
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