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.
Guided Zeroth-Order Methods for Stochastic Non-convex Problems with Decision-Dependent Distributions
Authors: Yuya Hikima, Hiroshi Sawada, Akinori Fujino
ICML 2025 | Venue PDF | LLM Run Details | Input Tokens: 31,340 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,713 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 | Our simulation experiments on multiple applications show that our methods output solutions with lower objective values than the existing zeroth-order methods do. |
| Researcher Affiliation | Industry | 1Communication Science Laboratories, NTT Corporation, Kyoto, Japan. Correspondence to: Yuya Hikima <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Guided zeroth-order method with new samples Algorithm 2 Guided zeroth-order method with historical samples |
| Open Source Code | No | The program code was implemented in Python 3.12.2. The paper does not contain a specific link to a code repository or an explicit statement about open-sourcing the code for the methodology described. |
| Open Datasets | Yes | The data, New Product Sales Ranking , has been made publicly available by KSP-SP Co., Ltd, http://www.ksp-sp.com. Last accessed on January 28, 2025. We conducted experiments on the application of strategic classification with a real dataset from (Yeh & hui Lien, 2009).7 As with (Levanon & Rosenfeld, 2021), we used a preprocessed version of the data by (Ustun et al., 2019). |
| Dataset Splits | Yes | We divided 13272 data samples into a 12272-sample training set and 1000-sample test set in our experiments. |
| Hardware Specification | Yes | All experiments were conducted on a computer with Intel(R) Xeon(R) CPU E5-2697A v4 (2.60GHz) x2 and 512GB of memory RAM. |
| Software Dependencies | Yes | The program code was implemented in Python 3.12.2. |
| Experiment Setup | Yes | GZO-NS. This is Algorithm 1 with µ0 := 0.2, µmin := 0.0001, α0 = 0, βk := 0.01 0.95k, η = 0.95, γ = 0.98, mk := 30 + 2k, and nk := 30 + 2k, where k is the current iteration number. Details of the parameters can be found in Appendix A.1.2 and A.2.1. |