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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Cost-Sensitive Learning to Rank
Authors: Ryan McBride, Ke Wang, Zhouyang Ren, Wenyuan Li4570-4577
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run experiments to validate the benefits of our new solutions on both proprietary and public data sets. In experiments, we validate two claims: |
| Researcher Affiliation | Academia | Ryan Mc Bride, Ke Wang Simon Fraser University, BC, Canada EMAIL and EMAIL Zhouyang Ren, Wenyuan Li Chongqing University, Chongqing, China EMAIL and EMAIL |
| Pseudocode | No | The paper describes algorithms such as Cost-Sensitive MART, Cost-Sensitive Coordinate Ascent, and CS-Ada Rank conceptually, explaining how they adapt existing methods. However, it does not provide any explicitly labeled |
| Open Source Code | No | The paper mentions |
| Open Datasets | Yes | We consider two proprietary outage data sets and three public UCI data sets. Attributes and details on each data set are provided in Table 2. |
| Dataset Splits | Yes | Testing uses five-fold cross-validation via LETOR s separation of data with three folds used for training, one for validation, and one for testing (Qin et al. 2010). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | For the two outage data sets, we use ks of 10 and 50, based on domain knowledge on how many networks may be strengthened before a storm given a 24 hour lead time. For the other data sets, we use two ks: a low k (12.5% of the average number of instances in a list) and a more medium k (25% of the average list length). Rank Lib does not support missing values or categorical attributes so we removed any attribute with missing values and convert each categorical attribute with C categories into C binary attributes. |