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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data
Authors: Haitong Liu, Qiang Li, Hoi To Wai
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 32,273 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,917 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 | Numerical experiments verify our analysis. |
| Researcher Affiliation | Academia | 1Department of Computer Science, ETH Zurich. The research was primarily conducted as a student at The Chinese University of Hong Kong. 2Department of System Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China. |
| Pseudocode | Yes | Algorithm 1 DFO (λ) Algorithm |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code or links to a code repository. |
| Open Datasets | No | The paper describes generating synthetic data for its experiments (e.g., 'auto-regressive (AR) process' for Quartic Loss, 'Markovian Pricing Problem', 'Markovian Performative Regression') rather than using publicly available datasets with specified access information. |
| Dataset Splits | No | The paper does not explicitly specify training, validation, or test dataset splits. It discusses results based on 'number of samples i' or total samples observed during the experiments. |
| Hardware Specification | Yes | All experiments are conducted on a server with an Intel Xeon 6318 CPU |
| Software Dependencies | Yes | using Python 3.7. |
| Experiment Setup | Yes | Unless otherwise specified, we use the step size choices in (8) for DFO (λ). |