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..
Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators
Authors: Wenhai Cui, Xiaoting Ji, Linglong Kong, Xiaodong Yan
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 15,574 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,032 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 | Finally, the superior finite-sample performance is evaluated by simulation studies. |
| Researcher Affiliation | Academia | 1 Zhongtai Securities Institute for Financial Studies, Shandong University 2 Department of Mathematical and Statistical Sciences, University of Alberta 3 Shandong Province Key Laboratory of Financial Risk 4Shandong National Center for Applied Mathematics EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: TAB-based Opposite Online Learning; Algorithm 2: An Extended Two-sided Test |
| Open Source Code | No | The paper does not provide any explicit statement or link to open-source code for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic data for simulation studies (e.g., "streaming data is generated by the mean model, Z = θ0 + ϵ"), but it does not specify or provide access to a publicly available or open dataset. |
| Dataset Splits | No | The paper describes simulation parameters like T and B, but it does not specify explicit training, validation, or test dataset splits for model evaluation. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments or simulations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | Input: Sequential data St(t = 1, . . . , T) Set the number of bootstraps B Maximum number of iterations N Hyperparameter d0; γn is equal to γ1n α with γ1 > 0 and α (0.5, 1); T = 500, B = 50; B = 100, T = 1000; T = 200, N = 100, B = 30; T = 30, N = 500, B = 30 |