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.
Generalized No Free Lunch Theorem for Adversarial Robustness
Authors: Elvis Dohmatob
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,344 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,928 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 | 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) |