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].
Robust Survey Aggregation with Student-t Distribution and Sparse Representation
Authors: Qingtao Tang, Tao Dai, Li Niu, Yisen Wang, Shu-Tao Xia, Jianfei Cai
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our proposed method achieves significant improvement over the state-of-the-art methods on both synthetic and real datasets. |
| Researcher Affiliation | Academia | Department of Computer Science and Technology, Tsinghua University, China EMAIL; EMAIL School of Computer Science and Engineering, Nanyang Technological University, Singapore EMAIL; EMAIL |
| Pseudocode | No | The paper does not contain STRUCTURED PSEUDOCODE OR ALGORITHM BLOCKS (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide CONCRETE ACCESS TO SOURCE CODE (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | GSS. General Social Survey (GSS) is a well-known public survey dataset [Smith et al., 2015]. |
| Dataset Splits | No | The paper mentions varying 'sampling percentage from 3% to 30%' but does not provide SPECIFIC DATASET SPLIT INFORMATION (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide SPECIFIC HARDWARE DETAILS (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide SPECIFIC ANCILLARY SOFTWARE DETAILS (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions varying 'sampling percentage from 3% to 30%' but does not contain SPECIFIC EXPERIMENTAL SETUP DETAILS (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |