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It is important that consumers and regulators can verify the provenance of large neural models to evaluate their capabilities and risks.
Security against covert adversaries: Efficient protocols for realistic adversaries
Yonatan Aumann and Yehuda Lindell · 2007
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Improved delegation of computation using fully homomorphic encryption
Kai-Min Chung, Yael Kalai, and Salil Vadhan · 2010
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An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
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Openwebtext corpus, 2019
Aaron Gokaslan, Vanya Cohen, Ellie Pavlick, and Stefanie Tellex · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin · 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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The Pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy · 2020
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Proof-of-learning: Definitions and practice
Hengrui Jia, Mohammad Yaghini, Christopher A Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, and Nicolas Papernot · 2021
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Manipulating sgd with data ordering attacks
Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A Erdogdu, and Ross J Anderson · 2021
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Detecting ai trojans using meta neural analysis
Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A Gunter, and Bo Li · 2021
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Counterfactual memorization in neural language models
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, and Nicholas Carlini · 2021
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Git re-basin: Merging models modulo permutation symmetries
Samuel K Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa · 2022
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Architectural backdoors in neural networks
Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, and Nicolas Papernot · 2022
Forgeability and membership inference attacks
Zhifeng Kong, Amrita Roy Chowdhury, and Kamalika Chaudhuri · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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On the necessity of auditable algorithmic definitions for machine unlearning
Anvith Thudi, Hengrui Jia, Ilia Shumailov, and Nicolas Papernot · 2022
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Diffusion models: A comprehensive survey of methods and applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang · 2022
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“adversarial examples” for proof-of-learning
Rui Zhang, Jian Liu, Yuan Ding, Zhibo Wang, Qingbiao Wu, and Kui Ren · 2022
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Cited alongside, same era.
On the fundamental limits of formally (dis) proving robustness in proof-of-learning
Congyu Fang, Hengrui Jia, Anvith Thudi, Mohammad Yaghini, Christopher A Choquette-Choo, Natalie Dullerud, Varun Chandrasekaran, and Nicolas Papernot · 2022
Cited alongside, same era.
Planting undetectable backdoors in machine learning models
Shafi Goldwasser, Michael P Kim, Vinod Vaikuntanathan, and Or Zamir · 2022
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An overview of backdoor attacks against deep neural networks and possible defences
Wei Guo, Benedetta Tondi, and Mauro Barni · 2022
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Deconstructing distributions: A pointwise framework of learning
Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, and Preetum Nakkiran · 2022
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Stella Biderman, USVSN Sai Prashanth, Lintang Sutawika, Hailey Schoelkopf, Quentin Anthony, Shivanshu Purohit, and Edward Raf · 2023
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Pythia: A suite for analyzing large language models across training and scaling, 2023
Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal · 2023
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Update on ARC’s recent eval efforts, March 2023
Alignment Research Center · 2023
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Yonadav Shavit · 2023
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