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Influence functions approximate the "influences" of training data-points for test predictions and have a wide variety of applications.
Fine-grained sentiment analysis with faithful attention
Ruiqi Zhong, Steven Shao, and Kathleen McKeown. 2019 · 1908
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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What would elsa do? freezing layers during transformer fine-tuning
Jaejun Lee, Raphael Tang, and Jimmy Lin. 2019 · 1911
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Detection of influential observation in linear regression
R Dennis Cook. 1977 · 1977
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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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Assessment of local influence
R Dennis Cook. 1986 · 1986
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Compositional explanations of neurons
Jesse Mu and Jacob Andreas. 2020 · 2006
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The elements of statistical learning: data mining, inference, and prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009 · 2009
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Pair the dots: Jointly examining training history and test stimuli for model interpretability
Yuxian Meng, C. Fan, Zijun Sun, E. Hovy, Fei Wu, and J. Li. 2020 · 2010
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Explaining and improving model behavior with k nearest neighbor representations
Nazneen Fatema Rajani, Ben Krause, Wengpeng Yin, Tong Niu, Richard Socher, and Caiming Xiong. 2020 · 2010
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Measuring association between labels and free-text rationales
Sarah Wiegreffe, Ana Marasovic, and Noah A. Smith. 2020 · 2010
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. 2020 · 2012
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Sara Beery, et al. 2020 · 2012
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Evaluating explanations: How much do explanations from the teacher aid students?
Danish Pruthi, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig, and William W. Cohen. 2020 · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
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Visualizing and Understanding Neural Models in NLP
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016a · 2016
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The mythos of model interpretability
Zachary C. Lipton. 2016 · 2016
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Second-order stochastic optimization for machine learning in linear time
Naman Agarwal, Brian Bullins, and Elad Hazan. 2017 · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
Earlier work this paper cites.
Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2017 · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 2017 · 2017
Cited alongside, same era.
A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017 · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg. 2017 · 2017
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 · 2019
Later among the works it cites.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
Later among the works it cites.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 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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Allennlp interpret: A framework for explaining predictions of nlp models
Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matt Gardner, and Sameer Singh. 2019 · 2019
Later among the works it cites.
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Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
Cited alongside, same era.
Pathologies of neural models make interpretations difficult
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber. 2018 · 2018
Cited alongside, same era.
xgems: Generating examplars to explain black-box models
Shalmali Joshi, Oluwasanmi Koyejo, Been Kim, and Joydeep Ghosh. 2018 · 2018
Cited alongside, same era.
Stronger data poisoning attacks break data sanitization defenses
Pang Wei Koh, Jacob Steinhardt, and Percy Liang. 2018 · 2018
Cited alongside, same era.
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
Nicolas Papernot and Patrick McDaniel. 2018 · 2018
Cited alongside, same era.
Explaingan: Model explanation via decision boundary crossing transformations
Pouya Samangouei, Ardavan Saeedi, Liam Nakagawa, and Nathan Silberman. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman. 2018 · 2018
Cited alongside, same era.
Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
Later among the works it cites.
On second-order group influence functions for black-box predictions
Samyadeep Basu, Xuchen You, and Soheil Feizi. 2020 · 2020
Closest in time.
How do decisions emerge across layers in neural models? interpretation with differentiable masking
Nicola De Cao, Michael Sejr Schlichtkrull, Wilker Aziz, and Ivan Titov. 2020 · 2020
Closest in time.
What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang. 2020 · 2020
Closest in time.
Explaining black box predictions and unveiling data artifacts through influence functions
Xiaochuang Han, Byron C. Wallace, and Yulia Tsvetkov. 2020 · 2020
Closest in time.
Evaluating explainable ai: Which algorithmic explanations help users predict model behavior?
Peter Hase and Mohit Bansal. 2020 · 2020
Closest in time.
Leakage-adjusted simulatability: Can models generate non-trivial explanations of their behavior in natural language?
Peter Hase, Shiyue Zhang, Harry Xie, and Mohit Bansal. 2020 · 2020
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Efficient estimation of influence of a training instance
Sosuke Kobayashi, Sho Yokoi, Jun Suzuki, and Kentaro Inui. 2020 · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan. 2020 · 2020
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Adversarial nli: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
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Beyond accuracy: Behavioral testing of nlp models with checklist
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
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The explanation game: Towards prediction explainability through sparse communication
Marcos Treviso and André F. T. Martins. 2020 · 2020
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Generative data augmentation for commonsense reasoning
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, and Doug Downey. 2020 · 2020
Closest in time.
Influence functions in deep learning are fragile
Samyadeep Basu, Phil Pope, and Soheil Feizi. 2021 · 2021
Closest in time.