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In this work, we study the computational complexity of determining whether a machine learning model that perfectly fits the training data will generalizes to unseen data.
On the computational complexity of algorithms
Juris Hartmanis and Richard E Stearns · 1965
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Universal classes of hash functions
J Lawrence Carter and Mark N Wegman · 1979
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The complexity of computing the permanent
Leslie G Valiant · 1979
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Unique signatures and verifiable random functions from the dh-ddh separation
Anna Lysyanskaya · 2002
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Derandomizing polynomial identity tests means proving circuit lower bounds
Valentine Kabanets and Russell Impagliazzo · 2004
Earlier work this paper cites.
Uniform direct product theorems: simplified, optimized, and derandomized
Russell Impagliazzo, Ragesh Jaiswal, Valentine Kabanets, and Avi Wigderson · 2010
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On the (im) possibility of obfuscating programs
Boaz Barak, Oded Goldreich, Russell Impagliazzo, Steven Rudich, Amit Sahai, Salil Vadhan, and Ke Yang · 2012
Cited alongside, same era.
Spectrally-normalized margin bounds for neural networks
Peter Bartlett, Dylan J Foster, and Matus Telgarsky · 2017
Cited alongside, same era.
Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2018
Cited alongside, same era.
The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C Lipton · 2018
Later among the works it cites.
Quantifying interpretability and trust in machine learning systems
Philipp Schmidt and Felix Biessmann · 2019
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Robustness in machine learning explanations: does it matter?
Leif Hancox-Li · 2020
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Indistinguishability obfuscation from well-founded assumptions
Aayush Jain, Huijia Lin, and Amit Sahai · 2021
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