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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Statistical Online Inference Approach in Averaged Stochastic Approximation
Authors: Chuhan Xie, Zhihua Zhang
NeurIPS 2022 | Venue PDF | 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 EMAIL Zhihua Zhang School of Mathematical Sciences Peking University Beijing, 100871, China EMAIL |
| 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. |