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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adversarial Sequence Tagging
Authors: Jia Li, Kaiser Asif, Hong Wang, Brian D. Ziebart, Tanya Berger-Wolf
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate the effectiveness of our proposed AST model. Table 3: Per-variable accuracy for the three approaches on different datasets. Table 4 shows the amount of time required to make predictions for all of the testing sequences. |
| Researcher Affiliation | Academia | Department of Computer Science, University of Illinois at Chicago, Chicago, IL EMAIL |
| Pseudocode | Yes | Algorithm 1 Single Oracle Game Solver. Algorithm 2 Parameter Estimation Algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described in the paper. |
| Open Datasets | Yes | Human Activity Recognition Dataset [Reyes-Ortiz et al., 2015]. Baboon Activity Recognition Dataset [Strandburg-Peshkin et al., 2015; Crofoot et al., 2015]. FAQ Segmentation Dataset [Mc Callum et al., 2000]. |
| Dataset Splits | Yes | We selected the regularization weights using a validation set (approximately 10% of the data). We use a validation set of 10% of the data for selecting the parameter c which controls the trade-off between slack and the magnitude of the weights vectors, and default parameters for the remaining settings. |
| Hardware Specification | No | The paper does not provide specific hardware details (like exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like LBFGS, SVM hmm, SVM light, and Gurobi, but does not provide specific version numbers for these software components to ensure reproducibility. |
| Experiment Setup | No | The paper mentions using stochastic gradient descent and selecting regularization weights and a parameter 'c' using a validation set, but it does not provide specific values for hyperparameters or detailed training configurations (e.g., learning rate, batch size, number of epochs). |