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..
Time-independent Generalization Bounds for SGLD in Non-convex Settings
Authors: Tyler Farghly, Patrick Rebeschini
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,514 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,197 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We establish generalization error bounds for stochastic gradient Langevin dynamics (SGLD) with constant learning rate under the assumptions of dissipativity and smoothness, a setting that has received increased attention in the sampling/optimization literature. Unlike existing bounds for SGLD in non-convex settings, ours are time-independent and decay to zero as the sample size increases. Using the framework of uniform stability, we establish time-independent bounds by exploiting the Wasserstein contraction property of the Langevin diffusion, which also allows us to circumvent the need to bound gradients using Lipschitz-like assumptions. Our analysis also supports variants of SGLD that use different discretization methods, incorporate Euclidean projections, or use non-isotropic noise. |
| Researcher Affiliation | Academia | Tyler Farghly Department of Statistics University of Oxford EMAIL Patrick Rebeschini Department of Statistics University of Oxford EMAIL |
| Pseudocode | No | The paper defines algorithms using mathematical equations (e.g., equation 1) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a specific dataset. Therefore, no information about public dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments or implementations that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical bounds and analyses, not on empirical experimental setups with hyperparameters or training configurations. |