Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators
Authors: Wenhai Cui, Xiaoting Ji, Linglong Kong, Xiaodong Yan
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {cuiwenhai, jixiaoting}@mail.sdu.edu.cn, lkong@ualberta.ca, yanxiaodong@sdu.edu.cn |
| 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 |