ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-Label Classification
Authors: Sima Behpour, Wei Xing, Brian Ziebart
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct multi-label and semi-supervised binary prediction experiments that demonstrate the beneļ¬ts of our approach. |
| Researcher Affiliation | Academia | Sima Behpour, Wei Xing, Brian D. Ziebart Department of Computer Science University Of Illinois at Chicago {sbehpo2,wxing3,bziebart}@uic.edu |
| Pseudocode | Yes | Algorithm 1 Double Oracle Algorithm for ARC Equilibria |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We first consider cut-based semi-supervised classification (Blum and Chawla 2001) using four datasets from the UCI repository (Lichman 2013). Most of the features we employ as unary and pairwise features are taken from the Mulan dataset (Tsoumakas et al. 2011). |
| Dataset Splits | Yes | We perform 10-fold cross-validation and report both the mean and standard deviation of the Hamming loss, except for the extremely large NUS-WIDE dataset, for which we only compute the Hamming loss for a singe testing sample due to its size. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or cluster specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Mulan' and 'word2vec' but does not specify their version numbers or any other software dependencies with specific versions. |
| Experiment Setup | No | The paper mentions using "standard gradient-based methods" for optimization but does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or training schedules. |