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Transfer learning has become an increasingly popular technique in machine learning as a way to leverage a pretrained model trained for one task to assist with building a finetuned model for a related task.
“White-box vs Black-box: Bayes Optimal Strategies for Membership Inference”, 2019
Alexandre Sablayrolles, Matthijs Douze, Yann Ollivier, Cordelia Schmid and Hervé Jégou · 1908
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“GAN-Leaks: A Taxonomy of Membership Inference Attacks against GANs”
Dingfan Chen, Ning Yu, Yang Zhang and Mario Fritz · 1909
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“Label-Only Membership Inference Attacks”
Christopher. Choquette-Choo, Florian Tramer, Nicholas Carlini and Nicolas Papernot · 1974
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“Revealing Information While Preserving Privacy”
Irit Dinur and Kobbi Nissim · 2003
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“An introduction to ROC analysis” ROC Analysis in Pattern Recognition
Tom Fawcett · 2005
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“Calibrating Noise to Sensitivity in Private Data Analysis”
Cynthia Dwork, Frank McSherry, Kobbi Nissim and Adam Smith · 2006
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“Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI”, 2020
Amirhossein Tavanaei · 2007
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“What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation”, 2020
Vitaly Feldman and Chiyuan Zhang · 2008
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“Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays”
Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John Pearson, Dietrich Stephan, Stanley Nelson and David Craig · 2008
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“ImageNet: A large-scale hierarchical image database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
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“Learning Multiple Layers of Features from Tiny Images”, 2009
Alex Krizhevsky · 2009
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“Genomic privacy and limits of individual detection in a pool”
Sriram Sankararaman, Guillaume Obozinski, Michael Jordan and Eran Halperin · 2009
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Yang Zou, Zhikun Zhang, Michael Backes and Yang Zhang · 2009
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“TransMIA: Membership Inference Attacks Using Transfer Shadow Training”
Seira Hidano, Yusuke Kawamoto and Takao Murakami · 2011
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“Cats and dogs”
Omkar Parkhi, Andrea Vedaldi, Andrew Zisserman and C.. Jawahar · 2012
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“Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers”
Giuseppe Ateniese, Luigi. Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali and Giovanni Felici · 2015
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“Robust Traceability from Trace Amounts”
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman and Salil Vadhan · 2015
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“Robust Traceability from Trace Amounts”
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman and Salil Vadhan · 2015
Earlier work this paper cites.
“Deep Residual Learning for Image Recognition”, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2015
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“Character-level Convolutional Networks for Text Classification”
Xiang Zhang, Junbo Zhao and Yann LeCun · 2015
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“Character-level Convolutional Networks for Text Classification”
Xiang Zhang, Junbo Zhao and Yann LeCun · 2015
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“Deep Learning with Differential Privacy”
Martin Abadi, Andy Chu, Ian Goodfellow, H. McMahan, Ilya Mironov, Kunal Talwar and Li Zhang · 2016
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“Pointer Sentinel Mixture Models”, 2016
Stephen Merity, Caiming Xiong, James Bradbury and Richard Socher · 2016
Cited alongside, same era.
“Membership Inference Attacks against Machine Learning Models”
Reza Shokri, Marco Stronati and Vitaly Shmatikov · 2016
Cited alongside, same era.
“Decoupled Weight Decay Regularization”
Ilya Loshchilov and Frank Hutter · 2017
Cited alongside, same era.
“SGDR: Stochastic Gradient Descent with Warm Restarts”
Ilya Loshchilov and Frank Hutter · 2017
Cited alongside, same era.
“Revisiting the unreasonable effectiveness of data”
July Tuesday and Computer Networks · 2017
Cited alongside, same era.
“On the Opportunities and Risks of Foundation Models”, 2022
Rishi Bommasani et al · 2022
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“Membership Inference Attacks From First Principles”
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis and Florian Tramèr · 2022
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“SNAP: Efficient Extraction of Private Properties with Poisoning”, 2022
Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr and Jonathan Ullman · 2022
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“Mixed Differential Privacy in Computer Vision”, 2022
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns and Stefano Soatto · 2022
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“Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping”, 2022
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Sergey Zagoruyko and Nikos Komodakis · 2017
Cited alongside, same era.
“Understanding deep learning requires rethinking generalization”
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht and Oriol Vinyals · 2017
Cited alongside, same era.
“The Natural Auditor: How To Tell If Someone Used Your Words To Train Their Model”
Congzheng Song and Vitaly Shmatikov · 2018
Cited alongside, same era.
“Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting”, 2018, pp. 268–282
Samuel Yeom, Irene Giacomelli, Matt Fredrikson and Somesh Jha · 2018
Cited alongside, same era.
“Reconciling modern machine-learning practice and the classical bias–variance trade-off”
Mikhail Belkin, Daniel Hsu, Siyuan Ma and Soumik Mandal · 2019
Cited alongside, same era.
“Transformer-XL: Attentive Language Models beyond a Fixed-Length Context”
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le and Ruslan Salakhutdinov · 2019
Cited alongside, same era.
“Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning”
Milad Nasr, Reza Shokri and Amir Houmansadr · 2019
Cited alongside, same era.
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Lee, Arturs Backurs, Nenghai Yu and Jiang Bian · 2022
Later among the works it cites.
“Caltech 101”
Fei-Fei Li, Marco Andreeto, Marc’Aurelio Ranzato and Pietro Perona · 2022
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“Large Language Models Can Be Strong Differentially Private Learners”
Xuechen Li, Florian Tramèr, Percy Liang and Tatsunori Hashimoto · 2022
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“Considerations for Differentially Private Learning with Large-Scale Public Pretraining”
Florian Tramèr, Gautam Kamath and Nicholas Carlini · 2022
Later among the works it cites.
“Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets”
Florian Tramèr, Reza Shokri, Ayrton San, Hoang Le, Matthew Jagielski, Sanghyun Hong and Nicholas Carlini · 2022
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“A Study of Face Obfuscation in ImageNet”
Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng and Olga Russakovsky · 2022
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“Enhanced Membership Inference Attacks against Machine Learning Models”
Jiayuan Ye, Aadyaa Maddi, Sasi Murakonda, Vincent Bindschaedler and Reza Shokri · 2022
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“Differentially Private Fine-tuning of Language Models”
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan Kulkarni, Yin Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin and Huishuai Zhang · 2022
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“Differentially private Bias-Term Only Fine-tuning of Foundation Models”, 2023
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha and George Karypis · 2023
Closest in time.
“Extracting Training Data from Diffusion Models”
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito and Eric Wallace · 2023
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“RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset”, 2023
Together Computer · 2023
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“Why Is Public Pretraining Necessary for Private Model Training?”, 2023
Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Thakurta and Lun Wang · 2023
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“Students Parrot Their Teachers: Membership Inference on Model Distillation”, 2023
Matthew Jagielski, Milad Nasr, Christopher Choquette-Choo, Katherine Lee and Nicholas Carlini · 2023
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“Measuring Forgetting of Memorized Training Examples”
Matthew Jagielski, Om Thakkar, Florian Tramer, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot and Chiyuan Zhang · 2023
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“How to combine membership-inference attacks on multiple updated machine learning models”
Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan Ullman and Roxana Geambasu · 2023
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“Transformer XL — huggingface.co” [Accessed 19-May-2023], https://huggingface.co/docs/transformers/model_doc/transfo-xl
2023
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“EncoderMI: Membership Inference against Pre-Trained Encoders in Contrastive Learning”
Hongbin Liu, Jinyuan Jia, Wenjie Qu and Neil Gong · 2095
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