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].
Interpretable Graph-Based Semi-Supervised Learning via Flows
Authors: Raif Rustamov, James Klosowski
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an empirical evaluation both on synthetic and real data (Section 6). |
| Researcher Affiliation | Industry | Raif M. Rustamov, James T. Klosowski AT&T Labs Research |
| Pseudocode | No | The paper describes algorithms (e.g., ADMM in Section 5) but does not present them in a structured pseudocode block or algorithm figure. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Next we test the proposed method on high-dimensional image and text datasets used in (Zhou and Belkin 2011), including MNIST 3vs8, MNIST 4vs9, aut-avn, ccat, gcat, pcmac, and real-sim. |
| Dataset Splits | Yes | In each run we use nl = 50 labeled samples; in addition, we withheld 50 samples for validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as CPU/GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions 'ADMM' and 'Graphviz' but does not specify version numbers for any software dependencies, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | For p-Voltages, the value of p for each run is chosen from {1.0625, 1.125, 1.25, 1.5, 2} using the best performing value on the validation samples... For iterated Laplacian method, m is chosen from {1, 2, 4, 8, 16}... For the flow approach, the value of λ is chosen from {0, 0.025, 0.05, 0.1, 0.2}... |