On the Generalization Analysis of Adversarial Learning
Authors: Waleed Mustafa, Yunwen Lei, Marius Kloft
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the generalization properties of adversarial learning. In particular, we derive high-probability generalization bounds on the adversarial risk in terms of the empirical adversarial risk, the complexity of the function class, and the adversarial noise set. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Kaiserslautern, Germany 2School of Computer Science, University of Birmingham, United Kingdom. |
| Pseudocode | No | The paper contains mathematical derivations, lemmas, and theorems, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing the source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and derives generalization bounds; it does not perform empirical evaluations on specific named datasets, and therefore, no information regarding public dataset access is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data, hence there is no information provided about dataset splits (train/validation/test). |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments; therefore, there is no mention of specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe an implementation or empirical experiments, therefore it does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not describe empirical experiments; therefore, no specific details about an experimental setup, such as hyperparameters or training configurations, are provided. |