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.
Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets
Authors: Dan Garber, Elad Hazan
ICML 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,144 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,019 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 | This paper focuses on proving new convergence rates for the Frank-Wolfe method under specific conditions. It presents mathematical proofs (e.g., Theorem 2, Lemma 1), analyzes definitions (smoothness, strong convexity), and discusses theoretical implications, rather than conducting empirical studies or experiments. There are no mentions of datasets, experimental setups, or performance metrics from actual runs. |
| Researcher Affiliation | Academia | Dan Garber EMAIL Technion Israel Institute of Technology Elad Hazan EMAIL Princeton University |
| Pseudocode | Yes | Algorithm 1 Frank-Wolfe Algorithm |
| Open Source Code | No | The paper does not provide explicit statements or links indicating the release of open-source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not involve empirical training on datasets, therefore no public dataset information is provided. |
| Dataset Splits | No | This is a theoretical paper and does not involve experimental validation on datasets, therefore no dataset split information is provided. |
| Hardware Specification | No | This is a theoretical paper and does not report on experiments or hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not report on experiments or software dependencies with specific version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with specific hyperparameters or configurations. |