Stochastic Cubic Regularization for Fast Nonconvex Optimization
Authors: Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {nilesh_tripuraneni,mitchell,chijin,regier}@berkeley.edu jordan@cs.berkeley.edu 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 final 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. |