Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data
Authors: Haitong Liu, Qiang Li, Hoi To Wai
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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). |