A Statistical Online Inference Approach in Averaged Stochastic Approximation
Authors: Chuhan Xie, Zhihua Zhang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to perform inference with both random scaling and other traditional inference methods, and finds that the former has a more accurate and robust performance. In this section we conduct the empirical analysis of the random scaling method via Monte Carlo experiments. |
| Researcher Affiliation | Academia | Chuhan Xie School of Mathematical Sciences Peking University Beijing, 100871, China ch_xie@pku.edu.cn Zhihua Zhang School of Mathematical Sciences Peking University Beijing, 100871, China zhzhang@math.pku.edu.cn |
| Pseudocode | No | The paper uses mathematical equations to describe algorithms (e.g., SA procedure, SGD updates, Q-learning), but it does not contain any structured pseudocode or algorithm blocks that are clearly labeled as such. |
| Open Source Code | Yes | The implementation code is available in https://github.com/bangoz/sa-inference. |
| Open Datasets | No | The data are generated from yt = x t β + εt, t ≥ 1, where xt is a d-dimensional covariate following the multivariate normal distribution N(0, Id), εt is the noise from N(0, 1), and β is equi-spaced on the interval [0, 1]. The data are generated from P(yt = 1) = 1 / (1 + exp(-x t β )), P(yt = 0) = exp(-x t β ) / (1 + exp(-x t β )), t ≥ 1. The paper uses synthetically generated data for its experiments and does not provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper states: "The simulation results are based on 1,000 replications." However, it does not specify explicit training/validation/test dataset splits, as the data is generated for each simulation rather than being drawn from a fixed dataset with predefined splits. |
| Hardware Specification | Yes | All experiments are run on Intel Xeon 14-core CPUs. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with their corresponding versions) that are needed to replicate the experiment. |
| Experiment Setup | Yes | The dimension of x is set as d = 5, 20. The constant learning rate is set as η = 0.01, 0.05, 0.1. The initial value of β0 is set as zero. The simulation results are based on 1,000 replications. |