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].
Generalized No Free Lunch Theorem for Adversarial Robustness
Authors: Elvis Dohmatob
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical bounds are demonstrated on both simulated and real data (MNIST). Section 4 presents experiments on both simulated and real data that confirm our theoretical results. |
| Researcher Affiliation | Industry | 1Criteo, Paris, France. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | MNIST dataset (Le Cun & Cortes, 2010) |
| Dataset Splits | No | The paper mentions 'held-out data' and 'train' but does not specify exact dataset split percentages or sample counts for training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions 'PyTorch' but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | The classifier h is a multi-layer perceptron with architecture 1000 200 100 2 and Re LU activations. trained a deep feed-forward CNN (architecture: Conv2d Conv2d 320 10) |