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..
On the Generalization Analysis of Adversarial Learning
Authors: Waleed Mustafa, Yunwen Lei, Marius Kloft
ICML 2022 | Venue PDF | 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. |