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..
Unbiased Objective Estimation in Predictive Optimization
Authors: Shinji Ito, Akihiro Yabe, Ryohei Fujimaki
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results for both artificial and real-world datasets demonstrate that our proposed approach successfully corrects the optimistic bias. |
| Researcher Affiliation | Industry | 1NEC Corporation. Correspondence to: Shinji Ito <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 k-fold cross-validation |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of its methodology. |
| Open Datasets | Yes | The real-world retail dataset used in (Ito & Fujimaki, 2017; 2016) contains sales information for a middle-size supermarket located in Tokyo.4 |
| Dataset Splits | Yes | We used the first 35 months (1063 samples) for training regression models and simulated the best price strategy for the next day 2014/12/1. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | Yes | We used GUROBI Optimizer 6.0.43 for portfolio optimization, and the algorithm in (Ito & Fujimaki, 2016) for price optimization. |
| Experiment Setup | Yes | We chose D = 50, N = 20, and λ = 1.0 for our simulation experiments. |