Demystifying Disagreement-on-the-Line in High Dimensions
Authors: Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on CIFAR-10-C, Tiny Image Net-C, and Camelyon17 are consistent with our theory and support the universality of the theoretical findings. |
| Researcher Affiliation | Academia | 1Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, PA, USA 2Department of Electrical and Systems Engineering, University of Pennsylvania, PA, USA 3Department of Statistics and Data Science, University of Pennsylvania, PA, USA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The associated code can be found at https://github.com/dh7401/RF-disagreement. |
| Open Datasets | Yes | We conduct experiments on the following datasets. ... CIFAR-10-C. Hendrycks & Dietterich (2018) introduced a corrupted version of CIFAR-10 (Krizhevsky et al., 2009). |
| Dataset Splits | No | The paper mentions 'training sample size n = 1000' and 'test the trained model on the rest of the sample' but does not specify a separate validation set or explicit train/validation/test splits with percentages or counts. |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware (e.g., GPU models, CPU types, or memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We use training sample size n = 1000, random features dimension N {3000, 4000, . . . , 49000}, input dimension d = 3072, regularization γ = 0. |