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..
Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions
Authors: Yuya Hikima, Akiko Takeda
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our simulation experiments with real data on a retail service application show that our methods output solutions with lower objective values than the conventional zeroth-order methods. |
| Researcher Affiliation | Academia | 1Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan 2Center for Advanced Intelligence Project, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Zeroth-order method with the improved one-point gradient estimator Algorithm 2: Zeroth-order method with the two-point gradient estimator |
| Open Source Code | Yes | Code https://github.com/Yuya-Hikima/AAAI25-Zeroth Order-Methods-for-Nonconvex-Stochastic-Problemswith-Decision-Dependent-Distributions |
| Open Datasets | Yes | We performed simulation experiments with real retail data from a supermarket service provider in Japan.5 All 5We used publicly available data, New Product Sales Ranking , provided by KSP-SP Co., Ltd, http://www.ksp-sp.com. Accessed August 15, 2024. |
| Dataset Splits | No | The paper mentions using real retail data but does not specify how this data was split into training, validation, or test sets for the experiments. |
| Hardware Specification | Yes | All experiments were conducted on a computer with an AMD EPYC 7413 24-Core Processor, 503.6 Gi B of RAM, and Ubuntu 20.04.6 LTS. |
| Software Dependencies | Yes | The program code was implemented in Python 3.8.3. |
| Experiment Setup | Yes | Proposed-1 (mini-batch). We implemented Algorithm 1 with µ0 := 0.19, µmin := 0.0001, c0 := P20 j=1 f(x0, ξj(x0)), smax := 10, β := 0.001 0.95k+1, γ = 0.95, mk = 30 + 2k, and M = 0.1, where k is the current iteration number. |