Unbiased Objective Estimation in Predictive Optimization
Authors: Shinji Ito, Akihiro Yabe, Ryohei Fujimaki
ICML 2018 | Conference PDF | Archive PDF | Plain Text | 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 <sito@me.jp.nec.com>. |
| 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. |