Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Without-Replacement Sampling for Stochastic Gradient Methods
Authors: Ohad Shamir
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,598 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,039 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 | In this paper, we provide competitive convergence guarantees for without-replacement sampling under several scenarios, focusing on the natural regime of few passes over the data. Moreover, we describe a useful application of these results in the context of distributed optimization with randomly-partitioned data, yielding a nearly-optimal algorithm for regularized least squares (in terms of both communication complexity and runtime complexity) under broad parameter regimes. Our proof techniques combine ideas from stochastic optimization, adversarial online learning and transductive learning theory, and can potentially be applied to other stochastic optimization and learning problems. |
| Researcher Affiliation | Academia | Ohad Shamir Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot, Israel EMAIL |
| Pseudocode | Yes | Algorithm 1 SVRG using Without-Replacement Sampling |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide links to a code repository. |
| Open Datasets | No | The paper focuses on theoretical analysis of optimization algorithms and does not describe or use specific public datasets for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets, thus it does not provide details on training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments, therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers required for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameter values or training configurations for empirical evaluation. |