Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Constrained Prescriptive Trees via Column Generation
Authors: Shivaram Subramanian, Wei Sun, Youssef Drissi, Markus Ettl4602-4610
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,718 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,025 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of our method with extensive experiments on both synthetic and real datasets. |
| Researcher Affiliation | Industry | IBM Research, Yorktown Heights, NY 10591, USA subshiva, sunw, youssefd, EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly presented. |
| Open Source Code | No | The paper uses third-party open-source tools (e.g., light GBM) but does not provide a link or explicit statement about the open-sourcing of its own methodology's code. |
| Open Datasets | Yes | using a publicly available dataset2 that contains over two years of household level transactions from a group of 2,500 households at a grocery retailer. A processed dataset with 97,295 rows which contain both purchases and no-purchases of strawberries is available online.3 |
| Dataset Splits | No | The paper mentions dividing data in halves for some experiments and varying training data size, but does not provide specific or consistent train/validation/test split proportions or methodologies for all experiments. |
| Hardware Specification | Yes | All experiments were run on an Intel 8-core i7 PC with 32GB RAM. |
| Software Dependencies | Yes | CPLEX 20.1 was used to solve the RMP. |
| Experiment Setup | Yes | The minimum number of samples per rule for SPMT is set to 10. K = 100 for KSP. |