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].
Optimal Pricing for Submodular Valuations with Bounded Curvature
Authors: Takanori Maehara, Yasushi Kawase, Hanna Sumita, Katsuya Tono, Ken-ichi Kawarabayashi
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct computational experiments on some synthetic and realistic datasets to evaluate the proposed pricing algorithms (Section 6). |
| Researcher Affiliation | Academia | Takanori Maehara Shizuoka University EMAIL Yasushi Kawase Tokyo Institute of Technology EMAIL Hanna Sumita National Institute of Informatics EMAIL Katsuya Tono University of Tokyo katsuya EMAIL Ken-ichi Kawarabayashi National Institute of Informatics k EMAIL |
| Pseudocode | Yes | Algorithm 1 Pricing algorithm for a single buyer. Algorithm 2 Pricing algorithm for multiple buyers. Algorithm 3 Pricing algorithm for collaborating buyers. |
| Open Source Code | No | The paper states, 'All codes were implemented in Python 2.7.3.' but does not provide any link or explicit statement that the code is publicly available. |
| Open Datasets | No | The paper mentions using 'two random synthetic networks (Uniform, Power Law) and three networks constructed from real-world datasets (Last.fm, Movie Lens, Book Crossing).' However, it does not provide specific links, DOIs, repositories, or formal citations for these datasets to indicate public availability. |
| Dataset Splits | No | The paper does not provide explicit details about training, validation, or test dataset splits (e.g., percentages, absolute counts, or citations to predefined splits). |
| Hardware Specification | Yes | All experiments were conducted on an Intel Xeon E5-2690 2.90GHz CPU (32 cores) with 256GB memory running Ubuntu 12.04. |
| Software Dependencies | Yes | All codes were implemented in Python 2.7.3. |
| Experiment Setup | No | The paper describes the types of experiments performed (e.g., comparison with baselines, scalability, relationship between activation probability and allocated channels) but does not provide specific experimental setup details such as hyperparameter values, model initialization, or training schedules. |