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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.
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
Authors: Dongruo Zhou, Quanquan Gu
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,939 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,373 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 study the lower bounds for smooth nonconvex finite-sum optimization, where the objective function is the average of n nonconvex component functions. We prove tight lower bounds for the complexity of finding -suboptimal point and -approximate stationary point in different settings, for a wide regime of the smallest eigenvalue of the Hessian of the objective function (or each component function). |
| Researcher Affiliation | Academia | Department of Computer Science, University of California, Los Angeles. Correspondence to: Quanquan Gu <EMAIL>. |
| Pseudocode | No | The paper is theoretical and focuses on mathematical proofs and lower bounds. It does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not present a new method with associated source code. Therefore, no information about open-source code for a new methodology is provided. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. Therefore, no information about datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve the use of datasets or their splits for experimental validation. Therefore, no information about validation splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not involve running experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not involve running experiments that would require specific software dependencies with version numbers. Therefore, no software dependencies are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments with specific setups, hyperparameters, or training configurations. Therefore, no experiment setup details are provided. |