Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Theoretical Analysis of Adversarial Learning: A Minimax Approach
Authors: Zhuozhuo Tu, Jingwei Zhang, Dacheng Tao
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,546 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,059 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose a general theoretical method for analyzing the risk bound in the presence of adversaries. Specifically, we try to fit the adversarial learning problem into the minimax framework. We first show that the original adversarial learning problem can be transformed into a minimax statistical learning problem by introducing a transport map between distributions. Then, we prove a new risk bound for this minimax problem in terms of covering numbers under a weak version of Lipschitz condition. |
| Researcher Affiliation | Academia | 1UBTECH Sydney AI Centre, School of Computer Science, The University of Sydney, Australia 2Department of Computer Science and Engineering, HKUST, Hong Kong |
| Pseudocode | No | The paper contains mathematical derivations and proofs but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statements about providing open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets, thus no information on public availability of training data is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |