Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Authors: Pranjal Awasthi, Natalie Frank, Mehryar Mohri
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give upper and lower bounds for the adversarial empirical Rademacher complexity of linear hypotheses with adversarial perturbations measured in lr-norm for an arbitrary r 1. We then extend our analysis to provide Rademacher complexity lower and upper bounds for a single Re LU unit. Finally, we give adversarial Rademacher complexity bounds for feed-forward neural networks with one hidden layer. We provide a brief sketch of the proof of Theorem 4 and provide the details in Appendix B. |
| Researcher Affiliation | Collaboration | 1Google Research and Rutgers University 2Courant Institute of Math. Sciences 3Google Research and Courant Institute of Math. Sciences. |
| Pseudocode | No | The paper is theoretical and focuses on mathematical proofs and bounds, therefore it does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include links to code repositories. |
| Open Datasets | No | The paper is theoretical and does not describe experiments performed on specific publicly available datasets. It refers to abstract 'samples' but not concrete datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, thus it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup or hardware used for computation. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational implementation or software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments, thus it does not provide details about an experimental setup, hyperparameters, or training configurations. |