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..
Effectiveness of Constant Stepsize in Markovian LSA and Statistical Inference
Authors: Dongyan (Lucy) Huo, Yudong Chen, Qiaomin Xie
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive numerical experiments and compare against classical inference approaches. Our results show that using a constant stepsize enjoys easy hyperparameter tuning, fast convergence, and consistently better CI coverage, especially when data is limited. |
| Researcher Affiliation | Academia | 1School of Operations Research and Information Engineering, Cornell University 2Department of Computer Sciences, University of Wisconsin-Madison 3Department of Industrial and Systems Engineering, University of Wisconsin-Madison |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly presented or labeled in the paper. |
| Open Source Code | No | No explicit statement or link providing access to open-source code for the described methodology. |
| Open Datasets | No | We consider LSA problems in dimension d = 5 for a finite state, irreducible, and aperiodic Markov chain with N = 10 states. We generate the transition probability P, and the functions A and b randomly; see the appendix for details. |
| Dataset Splits | No | The paper describes its data generation and processing steps (e.g., burn-in, batching) but does not specify standard training, validation, and test dataset splits in the context of model training. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | No specific software dependencies or their version numbers (e.g., Python, PyTorch, TensorFlow versions) are mentioned. |
| Experiment Setup | Yes | We consider LSA problems in dimension d = 5 for a finite state, irreducible, and aperiodic Markov chain with N = 10 states. We mainly study under constant stepsizes α = 0.2 and α = 0.02. The diminishing rate t 0.5 is chosen... The first b iterates (θ(α) t )b 1 t=0 are considered as initial burn-in and are not used in the inference procedure. For the remaining iterates, we divide them equally into K batches of size n. Within each batch, we discard the first n0( 0) iterates... |