Making Hard Problems Easier with Custom Data Distributions and Loss Regularization: A Case Study in Modular Arithmetic

Eshika Saxena; Alberto Alfarano; Emily Wenger; Kristin E. Lauter

2025 ICML ICML 2025

Making Hard Problems Easier with Custom Data Distributions and Loss Regularization: A Case Study in Modular Arithmetic

Abstract

No abstract available. No abstract was captured for this paper when its proceedings were scraped - most often an older edition published without one online. How the corpus is built.

Authors

Eshika Saxena, Alberto Alfarano, Emily Wenger, Kristin E. Lauter

Topics

Machine Learning > Optimization & Theory > Optimization Machine Learning > Application Areas > Efficient Computing

Keywords

modular arithmetic loss regularization post-quantum cryptography custom data distributions learning with errors (lwe) ml-based attacks

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