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 [1].
SGD without Replacement: Sharper Rates for General Smooth Convex Functions
Authors: Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | By using method of exchangeable pairs to bound Wasserstein distance, we provide the ο¬rst non-asymptotic results for SGDo when applied to general smooth, stronglyconvex functions. In particular, we show that SGDo converges at a rate of O(1/K2) while SGD is known to converge at O(1/K) rate, where K denotes the number of passes over data and is required to be large enough. Existing results for SGDo in this setting require additional Hessian Lipschitz assumption (G urb uzbalaban et al., 2015; Hao Chen & Sra, 2018). |
| Researcher Affiliation | Collaboration | Dheeraj Nagaraj 1 Praneeth Netrapalli 2 Prateek Jain 2 1Massachusetts Institute of Technology, Cambridge, Massachusetts, USA 2Microsoft Research, Bengaluru, Karnataka, India. |
| Pseudocode | Yes | Algorithm 1 SGD: SGD with replacement... Algorithm 2 SGDo: SGD without replacement |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing code for the work described in this paper, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | The paper is theoretical and does not involve experiments on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments, thus no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe software implementation or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |