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..
Stochastic Cubic Regularization for Fast Nonconvex Optimization
Authors: Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 14,574 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,056 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also empirically show that the stochastic cubic-regularized Newton method proposed in this paper performs favorably on both synthetic and real non-convex problems relative to state-of-the-art optimization methods. |
| Researcher Affiliation | Academia | Nilesh Tripuraneni Mitchell Stern Chi Jin Jeffrey Regier Michael I. Jordan EMAIL EMAIL University of California, Berkeley |
| Pseudocode | Yes | Algorithm 1 Stochastic Cubic Regularization (Meta-algorithm) Input: mini-batch sizes n1, n2, initialization x0, number of iterations Tout, and ο¬nal tolerance . |
| Open Source Code | No | The paper does not provide any links or explicit statements about the public release of its source code. |
| Open Datasets | Yes | training a deep autoencoder on MNIST [Le Cun and Cortes, 2010]. |
| Dataset Splits | No | The paper mentions training on MNIST but does not specify the train/validation/test split percentages or sample counts for each partition. |
| Hardware Specification | No | The paper mentions software used for implementation (TensorFlow) but does not provide specific details about the hardware (e.g., GPU or CPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions TensorFlow as the implementation framework but does not provide specific version numbers for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | The batch sizes and learning rates for each method are tuned separately to ensure a fair comparison; see Appendix D for details. |