Deception through Half-Truths
Authors: Andrew Estornell, Sanmay Das, Yevgeniy Vorobeychik10110-10117
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 Experiments As discussed in Section 5, our approximation scheme is to compute both the n-approximation mask and the heuristic mask, then take the one yielding the higher utility. Note that this combination clearly yields an n-approximation. As we now demonstrate, it is also significantly better in combination than either of the approaches by itself. Figure 1 (left) shows the results on random general and additive networks, and demonstrates that our combined algorithm significantly outperforms the approximation algorithm, largely on the strength of the heuristic, which is highly effective in these settings. |
| Researcher Affiliation | Academia | Andrew Estornell, Sanmay Das, Yevgeniy Vorobeychik Computer Science & Engineering, Washington University in St. Louis {aestornell, sanmay, yvorobeychik}@wustl.edu |
| Pseudocode | Yes | Algorithm 1 Approximation algorithm", "Algorithm 2 Heuristic algorithm |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions using "randomly generated networks" and "networks generated adversarially" for experiments but does not provide any specific information, links, or citations for public access to these datasets. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages, sample counts for training, validation, or testing) or cross-validation setup. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, libraries, or solvers used in the experiments. |
| Experiment Setup | No | The paper describes algorithmic approaches and theoretical results but does not provide specific experimental setup details such as hyperparameter values, model initialization, or training schedules. |