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.
Accelerated Multiplicative Weights Update Avoids Saddle Points Almost Always
Authors: Yi Feng, Ioannis Panageas, Xiao Wang
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 14,038 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,546 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 | 5 Experiments", "The experiments are designed to understand the behavior of A-MWU compared to classic MWU and Accelerated Mirror Descent (A-MD)...", "The experimental results indicate the following: As expected, all experiments have verified that A-MWU has a better convergence behavior and efficiency in escaping saddle points compared to classic MWU. |
| Researcher Affiliation | Academia | 1Shanghai University of Finance and Economics 2University of California, Irvine |
| Pseudocode | Yes | Algorithm 1 Single-Agent A-MWU |
| Open Source Code | No | No explicit statement about providing source code or a link to a code repository for the described methodology was found. |
| Open Datasets | No | The paper uses mathematical functions like 'Rosenbrock function', 'Bohachevsky function', 'Test function 1', and 'Test function 2' for experiments. These are not publicly available datasets in the form of files or repositories that can be accessed via a link or citation for reproducibility. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits. It mentions 'initial points' for the functions used in experiments but no data partitioning. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments were provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) were mentioned in the paper. |
| Experiment Setup | Yes | Parameter setting. In our experiments, we set the parameters as follows: β = 0.001 and µ = 1 for Rosenbrock function, Figure 1. β = 0.1 and µ = 1 for Bohachevsky function, Figure 2. β = 0.1 and µ = 0.2 for Test function 1, Figure 3. β = 0.001 and µ = 0.001 for Test function 2, Figure 4. β = 0.1 and µ = 0.5 for two-agent, Figure 5. ... and the step sizes are 0.01 for A-MWU, MWU and A-MD, see Figure 1. |