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..
Distributionally Robust Performative Optimization
Authors: Zhuangzhuang Jia, Yijie Wang, Roy Dong, Grani A. Hanasusanto
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments in strategic classification, revenue management, and portfolio optimization demonstrate significant performance gains over state-of-the-art baselines, highlighting the practical value of our approach. |
| Researcher Affiliation | Academia | 1Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana Champaign 2School of Economics and Management, Tongji University |
| Pseudocode | No | The paper describes the 'repeated robust risk minimization algorithm' conceptually in Section 3 and explains its steps, but does not provide a formally labeled pseudocode block or algorithm figure. |
| Open Source Code | No | The code is available upon request. |
| Open Datasets | Yes | We consider a simulated strategic classification problem from [40] using a class-balanced subset of a Kaggle credit scoring dataset [25]. |
| Dataset Splits | Yes | The training set is fixed at 200 samples, while approximately 3,600 data points are used for out-of-sample testing. |
| Hardware Specification | Yes | All experiments were conducted on a laptop equipped with a 6-core, 2.3 GHz Intel Core i7 CPU and 16 GB of RAM. |
| Software Dependencies | Yes | The optimization problems were implemented in Python 3.11. |
| Experiment Setup | Yes | All models are trained for 40 iterations, with the robust parameter set to 0.1 for all robust variants. |