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.
On Certifying Non-Uniform Bounds against Adversarial Attacks
Authors: Chen Liu, Ryota Tomioka, Volkan Cevher
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,664 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,583 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide experimental evidence in Section 4... In this Section, we compare our certified non-uniform bounds with uniform bounds. We also use our algorithm as a tool to explore the decision boundaries of different models. All the experiments here are implemented in the framework of Py Torch and can be finished within several hours on a single NVIDIA Tesla GPU machine. |
| Researcher Affiliation | Collaboration | 1EPFL, Lausanne, Switzerland 2Microsoft Research, Cambridge, UK. |
| Pseudocode | Yes | Algorithm 1 Bound Estimation |
| Open Source Code | No | The paper does not provide a direct link to the source code for the methodology described, nor does it explicitly state that the code will be made publicly available. |
| Open Datasets | Yes | real datasets, including MNIST, Fashion-MNIST and SVHN (Netzer et al., 2011). |
| Dataset Splits | Yes | 90% of the data points are in the training set and the rest are reserved for testing. |
| Hardware Specification | Yes | All the experiments here are implemented in the framework of Py Torch and can be finished within several hours on a single NVIDIA Tesla GPU machine. |
| Software Dependencies | No | The paper mentions 'Py Torch' as the framework for implementation but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We set the perturbation budget of PGD to be 0.1 and search for adversarial examples for 20 iterations. |