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.
Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem
Authors: Bingcong Li, Lingda Wang, Georgios B. Giannakis, Zhizhen Zhao8324-8331
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,613 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,689 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical tests on binary classification with different sparsity-promoting constraints demonstrate that the empirical performance of Extra FW is significantly better than FW, and even faster than Nesterov s accelerated gradient on certain datasets. For matrix completion, Extra FW enjoys smaller optimality gap, and lower rank than FW. and 4 Numerical Tests This section deals with numerical tests of Extra FW to showcase its effectiveness on different machine learning problems. |
| Researcher Affiliation | Academia | Bingcong Li,1 Lingda Wang,2 Georgios B. Giannakis,1 Zhizhen Zhao2 1University of Minnesota Twin Cities 2University of Illinois at Urbana-Champaign EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 FW (Frank and Wolfe 1956), Algorithm 2 AFW (Li et al. 2020), Algorithm 3 Extra FW |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. The provided arXiv link is for the paper itself, not source code. |
| Open Datasets | Yes | Datasets mnist2 and those from LIBSVM3 are used in the numerical tests. ... 2http://yann.lecun.com/exdb/mnist/ 3https://www.csie.ntu.edu.tw/~cjlin/libsvm/ and We test Extra FW on a widely used dataset, Movie Lens100K4. ... 4https://grouplens.org/datasets/movielens/100k/ |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning, specifically for training, validation, and testing sets. While it mentions datasets, it lacks explicit split details. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | In the simulation, R is tuned to obtain a solution that is almost as sparse as the dataset itself. |