Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Authors: Pranjal Awasthi, Natalie Frank, Mehryar Mohri
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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. |