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Modern machine learning models are complex and frequently encode surprising amounts of information about individual inputs.
On the probability in the tail of a binomial distribution
John E Littlewood · 1969
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On the probability in the tail of a binomial distribution
John E Littlewood · 1969
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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An information statistics approach to data stream and communication complexity
Ziv Bar-Yossef, Thathachar S Jayram, Ravi Kumar, and D Sivakumar · 2004
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An information statistics approach to data stream and communication complexity
Ziv Bar-Yossef, Thathachar S Jayram, Ravi Kumar, and D Sivakumar · 2004
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Practical privacy: the sulq framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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Practical privacy: the sulq framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith · 2006
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The limits of two-party differential privacy
Andrew McGregor, Ilya Mironov, Toniann Pitassi, Omer Reingold, Kunal Talwar, and Salil P. Vadhan · 2010
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The limits of two-party differential privacy
Andrew McGregor, Ilya Mironov, Toniann Pitassi, Omer Reingold, Kunal Talwar, and Salil P. Vadhan · 2010
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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An optimal lower bound on the communication complexity of gap-hamming-distance
Amit Chakrabarti and Oded Regev · 2012
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An optimal lower bound on the communication complexity of gap-hamming-distance
Amit Chakrabarti and Oded Regev · 2012
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On the communication complexity of sparse set disjointness and exists-equal problems
Mert Saglam and Gábor Tardos · 2013
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On the communication complexity of sparse set disjointness and exists-equal problems
Mert Saglam and Gábor Tardos · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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Sample complexity bounds on differentially private learning via communication complexity
Vitaly Feldman and David Xiao · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Speech and language processing. vol. 3, 2014
Dan Jurafsky and James H Martin · 2014
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Analysis of boolean functions
Ryan O’Donnell · 2014
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Capturing long-tail distributions of object subcategories
Xiangxin Zhu, Dragomir Anguelov, and Deva Ramanan · 2014
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
Sample complexity bounds on differentially private learning via communication complexity
Vitaly Feldman and David Xiao · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Speech and language processing. vol. 3, 2014
Dan Jurafsky and James H Martin · 2014
Earlier work this paper cites.
Analysis of boolean functions
Ryan O’Donnell · 2014
Earlier work this paper cites.
Capturing long-tail distributions of object subcategories
Xiangxin Zhu, Dragomir Anguelov, and Deva Ramanan · 2014
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Generalization in adaptive data analysis and holdout reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, and Aaron Roth · 2015
Cited alongside, same era.
Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
Cited alongside, same era.
Generalization in adaptive data analysis and holdout reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, and Aaron Roth · 2015
Cited alongside, same era.
Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
Max-information, differential privacy, and post-selection hypothesis testing
Ryan Rogers, Aaron Roth, Adam Smith, and Om Thakkar · 2016
Fast learning requires good memory: A time-space lower bound for parity learning
Ran Raz · 2018
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Private PAC learning implies finite littlestone dimension
Noga Alon, Roi Livni, Maryanthe Malliaris, and Shay Moran · 2019
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Characterizing the sample complexity of pure private learners
Amos Beimel, Kobbi Nissim, and Uri Stemmer · 2019
Later among the works it cites.
Communication complexity of estimating correlations
Uri Hadar, Jingbo Liu, Yury Polyanskiy, and Ofer Shayevitz · 2019
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Average-case information complexity of learning
Ido Nachum and Amir Yehudayoff · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
Max-information, differential privacy, and post-selection hypothesis testing
Ryan Rogers, Aaron Roth, Adam Smith, and Om Thakkar · 2016
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
Cited alongside, same era.
Asymptotic tail bound and applications
Thomas D Ahle · 2017
Cited alongside, same era.
A closer look at memorization in deep networks
Devansh Arpit, Stanislaw Jastrzkebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
Cited alongside, same era.
Later among the works it cites.
Overparameterized neural networks can implement associative memory
Adityanarayanan Radhakrishnan, Mikhail Belkin, and Caroline Uhler · 2019
Later among the works it cites.
Small relu networks are powerful memorizers: a tight analysis of memorization capacity
Chulhee Yun, Suvrit Sra, and Ali Jadbabaie · 2019
Later among the works it cites.
Identity crisis: Memorization and generalization under extreme overparameterization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Michael C Mozer, and Yoram Singer · 2019
Later among the works it cites.
Private PAC learning implies finite littlestone dimension
Noga Alon, Roi Livni, Maryanthe Malliaris, and Shay Moran · 2019
Later among the works it cites.
Characterizing the sample complexity of pure private learners
Amos Beimel, Kobbi Nissim, and Uri Stemmer · 2019
Later among the works it cites.
Communication complexity of estimating correlations
Uri Hadar, Jingbo Liu, Yury Polyanskiy, and Ofer Shayevitz · 2019
Later among the works it cites.
Average-case information complexity of learning
Ido Nachum and Amir Yehudayoff · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Later among the works it cites.
Overparameterized neural networks can implement associative memory
Adityanarayanan Radhakrishnan, Mikhail Belkin, and Caroline Uhler · 2019
Later among the works it cites.
Small relu networks are powerful memorizers: a tight analysis of memorization capacity
Chulhee Yun, Suvrit Sra, and Ali Jadbabaie · 2019
Later among the works it cites.
Identity crisis: Memorization and generalization under extreme overparameterization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Michael C Mozer, and Yoram Singer · 2019
Later among the works it cites.
Variational predictive information bottleneck
Alexander A Alemi · 2020
Closest in time.
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, Alina Oprea, and Colin Raffel · 2020
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The limits of pan privacy and shuffle privacy for learning and estimation
Albert Cheu and Jonathan Ullman · 2020
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Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 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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A limitation of the pac-bayes framework
Roi Livni and Shay Moran · 2020
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Communication Complexity: and Applications
Anup Rao and Amir Yehudayoff · 2020
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Variational predictive information bottleneck
Alexander A Alemi · 2020
Closest in time.
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, Alina Oprea, and Colin Raffel · 2020
Closest in time.
The limits of pan privacy and shuffle privacy for learning and estimation
Albert Cheu and Jonathan Ullman · 2020
Closest in time.
Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
Closest in time.
A limitation of the pac-bayes framework
Roi Livni and Shay Moran · 2020
Closest in time.
Communication Complexity: and Applications
Anup Rao and Amir Yehudayoff · 2020
Closest in time.