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.
Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction
Authors: Zijian Liu, Ta Duy Nguyen, Alina Ene, Huy Nguyen
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 45,398 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,387 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 | We demonstrate the superior performance of our algorithms compared with previous methods in experiments on realworld datasets. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Boston University 2Khoury College of Computer and Information Science, Northeastern University. |
| Pseudocode | Yes | Algorithm 1 Ada VRAE; Algorithm 2 Ada VRAG |
| Open Source Code | No | The paper mentions using the codebase of a prior work and provides a link to that (Dubois-Taine et al., 2021), but it does not state that its own code is open-source or provide a link for its own implementation. |
| Open Datasets | Yes | We experiment with binary classification on four standard LIBSVM datasets: a1a, mushrooms, w8a and phishing (Chang & Lin, 2011). |
| Dataset Splits | No | The paper mentions training objectives and evaluation but does not specify details of training, validation, and test splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions using the 'code base of (Dubois-Taine et al., 2021)' but does not list specific software dependencies with version numbers for its own implementation. |
| Experiment Setup | Yes | For the non-adaptive methods we chose the step size (or equivalently, the inverse of the smoothness parameter (1/β) for VRADA) via hyperparameter search over {0.01, 0.05, 0.1, 0.5, 1, 5, 10, 100}. For Ada SVRG, we used η = D/2R as recommended in the original paper. For Ada VRAE and Ada VRAG, we used γ = 0.01 and η = D/2 = R. Table 2 reports the hyperparameter choices used in the experiments. |