Adversarial Classification: Necessary Conditions and Geometric Flows
Authors: Nicolás García Trillos, Ryan Murray
JMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical examples illustrating these ideas are also presented. |
| Researcher Affiliation | Academia | Nicol as Garc ıa Trillos EMAIL Department of Statistics University of Wisconsin Madison, Wisconsin, USA Ryan Murray EMAIL Department of Mathematics North Carolina State University Raleigh, NC, USA |
| Pseudocode | No | The paper describes mathematical derivations and propositions but does not include any explicit pseudocode or algorithm blocks. For example, it mentions an "MBO scheme" as a numerical algorithm from prior work, but does not present its own structured pseudocode. |
| Open Source Code | No | The paper does not provide any concrete access information for source code, such as repository links or explicit statements of code release. It mentions using a "standard ODE solver in Python" and a "modified version of the scheme from (Merriman et al., 1992)" but these refer to external tools/methods rather than the authors' own implementation code. |
| Open Datasets | No | The paper uses synthetic data for its numerical examples, describing the probability distributions. For instance, in Section 4.1 it states: "Suppose that P(X dx|Y = 1) = φ(x)dx... and let P(X dx|Y = 0) = φ((x 2)/2)dx 2 ." In Section 6.3, it says: "We consider two different classes ρ1 N(( .5, 2), Σ) + N(( .5, .5), Σ) and ρ2 N((.5, .5), Σ) + N((.5, 2), Σ, where Σ = .2I, and w0 = w1 = .5." These are descriptions of data generation, not references to publicly available datasets. |
| Dataset Splits | No | The paper utilizes synthetic data generated from described distributions for its numerical examples. Therefore, the concept of specific training, validation, or test dataset splits is not applicable or mentioned. |
| Hardware Specification | No | The paper mentions running numerical examples using a "standard ODE solver in Python" and a "modified version of the scheme from (Merriman et al., 1992)" but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for these computations. |
| Software Dependencies | No | The paper mentions using a "standard ODE solver in Python" and a "modified version of the scheme from (Merriman et al., 1992)" for its numerical examples. However, it does not provide specific version numbers for Python or any of the solvers/schemes used, which is required for a reproducible description of ancillary software. |
| Experiment Setup | No | The paper describes the data distributions for its numerical examples (e.g., "Suppose that P(X dx|Y = 1) = φ(x)dx..." in Section 4.1, and "We consider two different classes ρ1 N(( .5, 2), Σ) + N(( .5, .5), Σ) and ρ2 N((.5, .5), Σ) + N((.5, 2), Σ, where Σ = .2I, and w0 = w1 = .5." in Section 6.3). However, it does not provide specific hyperparameters, training configurations, or system-level settings typically found in experimental setups for model training or evaluation. |