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Data attribution seeks to trace model outputs back to training data.
A value for n-person games
Lloyd S Shapley et al · 1953
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Residuals and influence in regression
R Dennis Cook and Sanford Weisberg · 1982
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Extensions of lipschitz mappings into hilbert space
William B. Johnson and Joram Lindenstrauss · 1984
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The proof and measurement of association between two things
Charles Spearman · 1987
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An introduction to the bootstrap
Roger W Johnson · 2001
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Robust statistics: the approach based on influence functions
Frank R Hampel, Elvezio M Ronchetti, Peter J Rousseeuw, and Werner A Stahel · 2011
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Model-agnostic interpretability of machine learning
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Optimal subsampling with influence functions
Daniel Ting and Eric Brochu · 2018
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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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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
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A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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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
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Relatif: Identifying explanatory training samples via relative influence
Elnaz Barshan, Marc-Etienne Brunet, and Gintare Karolina Dziugaite · 2020
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models
Fan Bao, Chongxuan Li, Jun Zhu, and Bo Zhang · 2022
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Identifying a training-set attack’s target using renormalized influence estimation
Zayd Hammoudeh and Daniel Lowd · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 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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Ridge regularization: An essential concept in data science
Trevor Hastie · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
New insights and perspectives on the natural gradient method
James Martens · 2020
Cited alongside, same era.
Estimating training data influence by tracing gradient descent
Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan · 2020
Cited alongside, same era.
Influence functions in deep learning are fragile
Samyadeep Basu, Philip Pope, and Soheil Feizi · 2021
Cited alongside, same era.
Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
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Shuming Kong, Yanyan Shen, and Linpeng Huang · 2022
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The artbench dataset: Benchmarking generative models with artworks
Peiyuan Liao, Xiuyu Li, Xihui Liu, and Kurt Keutzer · 2022
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Measuring the effect of training data on deep learning predictions via randomized experiments
Jinkun Lin, Anqi Zhang, Mathias Lécuyer, Jinyang Li, Aurojit Panda, and Siddhartha Sen · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Scaling up influence functions
Andrea Schioppa, Polina Zablotskaia, David Vilar, and Artem Sokolov · 2022
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An empirical study of memorization in nlp
Xiaosen Zheng and Jing Jiang · 2022
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Adapting and evaluating influence-estimation methods for gradient-boosted decision trees
Jonathan Brophy, Zayd Hammoudeh, and Daniel Lowd · 2023
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Training data attribution for diffusion models
Zheng Dai and David K Gifford · 2023
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The journey, not the destination: How data guides diffusion models
Kristian Georgiev, Joshua Vendrow, Hadi Salman, Sung Min Park, and Aleksander Madry · 2023
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Studying large language model generalization with influence functions
Roger Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, et al · 2023
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On memorization in diffusion models
Xiangming Gu, Chao Du, Tianyu Pang, Chongxuan Li, Min Lin, and Ye Wang · 2023
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Trak: Attributing model behavior at scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry · 2023
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Evaluating data attribution for text-to-image models
Sheng-Yu Wang, Alexei A Efros, Jun-Yan Zhu, and Richard Zhang · 2023
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Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
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