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..
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. |