Learners that Leak Little Information. - Raef Bassily; Shay Moran; Ido Nachum; Jonathan Shafer; Amir Yehudayoff (CoRR, 2017)

Learners that Use Little Information. - Raef Bassily; Shay Moran; Ido Nachum; Jonathan Shafer; Amir Yehudayoff (ALT, 2018)

A Direct Sum Result for the Information Complexity of Learning. - Ido Nachum; Jonathan Shafer; Amir Yehudayoff (COLT, 2018)

Average-Case Information Complexity of Learning. - Ido Nachum; Amir Yehudayoff (ALT, 2019)

On the Perceptron's Compression. - Shay Moran; Ido Nachum; Itai Panasoff; Amir Yehudayoff (CiE, 2020)

On Symmetry and Initialization for Neural Networks. - Ido Nachum; Amir Yehudayoff (LATIN, 2020)

Regularization by Misclassification in ReLU Neural Networks. - Elisabetta Cornacchia; Jan Hazla; Ido Nachum; Amir Yehudayoff (CoRR, 2021)

Almost-Reed-Muller Codes Achieve Constant Rates for Random Errors. - Emmanuel Abbe; Jan Hazla; Ido Nachum (IEEE Trans. Inf. Theory, 2021)

A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs. - Ido Nachum; Jan Hazla; Michael Gastpar; Anatoly Khina (ICLR, 2022)

Finite Littlestone Dimension Implies Finite Information Complexity. - Aditya Pradeep; Ido Nachum; Michael Gastpar (ISIT, 2022)

Fantastic Generalization Measures are Nowhere to be Found. - Michael Gastpar; Ido Nachum; Jonathan Shafer; Thomas Weinberger (ICLR, 2024)

Which Algorithms Have Tight Generalization Bounds? - Michael Gastpar; Ido Nachum; Jonathan Shafer; Thomas Weinberger (CoRR, 2024)

Batch Normalization Decomposed. - Ido Nachum; Marco Bondaschi; Michael Gastpar; Anatoly Khina (CoRR, 2024)