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Generative artificial intelligence (AI) systems are trained on large data corpora to generate new pieces of text, images, videos, and other media.
A value for n n -person games
Lloyd S Shapley · 1953
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Characterizations of an empirical influence function for detecting influential cases in regression
R Dennis Cook and Sanford Weisberg · 1980
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Value theory without efficiency
Pradeep Dubey, Abraham Neyman, and Robert James Weber · 1981
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Introduction to the shapley value
Alvin E Roth · 1988
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Games with permission structures: the conjunctive approach
Robert P Gilles, Guillermo Owen, and Rene van den Brink · 1992
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Learning multiple layers of features from tiny images
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Towards efficient data valuation based on the shapley value
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The pile: An 800gb dataset of diverse text for language modeling
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Replication-robust payoff-allocation for machine learning data markets
Dongge Han, Michael Wooldridge, Alex Rogers, Shruti Tople, Olga Ohrimenko, and Sebastian Tschiatschek · 2020
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Approximating the shapley value using stratified empirical bernstein sampling
Mark Alexander Burgess and Archie C Chapman · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2021
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A multilinear sampling algorithm to estimate shapley values
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Deduplicating training data makes language models better
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Measuring the effect of training data on deep learning predictions via randomized experiments
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Sampling permutations for shapley value estimation
Rory Mitchell, Joshua Cooper, Eibe Frank, and Geoffrey Holmes · 2022
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High-resolution image synthesis with latent diffusion models
Pro-rata vs user-centric in the music streaming industry
Xiaochang Lei · 2023
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Probabilistic copyright protection can fail for text-to-image generative models
Xiang Li, Qianli Shen, and Kenji Kawaguchi · 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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Copyright safety for generative ai
Matthew Sag · 2023
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Generative ai meets copyright
Pamela Samuelson · 2023
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Glaze: Protecting artists from style mimicry by text-to-image models
Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, and Ben Y Zhao · 2023
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Tianhao Wang, Yu Yang, and Ruoxi Jia · 2022
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Sparks of artificial general intelligence: Early experiments with gpt-4
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How to protect copyright data in optimization of large language models?
Timothy Chu, Zhao Song, and Chiwun Yang · 2023
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Computational copyright: Towards a royalty model for ai music generation platforms
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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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Provable copyright protection for generative models
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Data banzhaf: A robust data valuation framework for machine learning
Jiachen T Wang and Ruoxi Jia · 2023
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A note on” towards efficient data valuation based on the shapley value”
Jiachen T Wang and Ruoxi Jia · 2023
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Matching-based data valuation for generative model
Jiaxi Yang, Wenglong Deng, Benlin Liu, Yangsibo Huang, and Xiaoxiao Li · 2023
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Intriguing properties of data attribution on diffusion models
Xiaosen Zheng, Tianyu Pang, Chao Du, Jing Jiang, and Min Lin · 2023
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Tackling genai copyright issues: Originality estimation and genericization
Hiroaki Chiba-Okabe and Weijie J. Su · 2024
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Fantastic copyrighted beasts and how (not) to generate them
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Data shapley in one training run
Jiachen T Wang, Prateek Mittal, Dawn Song, and Ruoxi Jia · 2024
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