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 [1].
Mixed Nash Equilibria in the Adversarial Examples Game
Authors: Laurent Meunier, Meyer Scetbon, Rafael B Pinot, Jamal Atif, Yann Chevaleyre
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our findings with experiments on simulated and real datasets, namely CIFAR-10 an CIFAR-100 (Krizhevsky & Hinton, 2009). |
| Researcher Affiliation | Collaboration | Laurent Meunier * 1 2 Meyer Scetbon * 3 Rafael Pinot 4 Jamal Atif 1 Yann Chevaleyre 1 1 Miles Team, LAMSADE, Université Paris-Dauphine, Paris, France 2 Facebook AI Research, Paris, France 3 CREST, ENSAE, Paris, France 4 Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. |
| Pseudocode | Yes | Algorithm 1 Oracle-based Algorithm; Algorithm 2 Adversarial Training for Mixtures |
| Open Source Code | No | The paper does not provide a link to open-source code or explicitly state that the code for the methodology is publicly available. |
| Open Datasets | Yes | We validate our findings with experiments on simulated and real datasets, namely CIFAR-10 an CIFAR-100 (Krizhevsky & Hinton, 2009). We sample 1000 training points from this distribution... |
| Dataset Splits | No | The paper mentions selecting models to avoid overfitting (Rice et al., 2020), but it does not specify a distinct 'validation' dataset split with percentages or counts. |
| Hardware Specification | Yes | We trained our models with a batch of size 1024 on 8 Nvidia V100 GPUs. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as Python versions, deep learning frameworks (e.g., PyTorch, TensorFlow) with their versions, or other libraries. |
| Experiment Setup | Yes | We trained from 1 to 4 Res Net18 (He et al., 2016) models on 200 epochs per model. The attack we used in the inner maximization of the training is an adapted (adaptative) version of PGD for mixtures of classifiers with 10 steps. We trained our models with a batch of size 1024 on 8 Nvidia V100 GPUs. |