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.
Non-convex Stochastic Composite Optimization with Polyak Momentum
Authors: Yuan Gao, Anton Rodomanov, Sebastian U Stich
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 25,243 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,850 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 | Finally, we provide numerical experiments to validate our theoretical results. |
| Researcher Affiliation | Academia | 1CISPA, Saarbrücken, Germany 2Universität des Saarlandes. |
| Pseudocode | Yes | Algorithm 1 Proximal Gradient Method with Polyak Momentum |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the open-sourcing of the code for the methodology described in this paper. |
| Open Datasets | Yes | We evaluate the performances of Algorithm 1 and the vanilla stochastic proximal gradient method on the Cifar-10 dataset (Krizhevsky et al., 2014) with the Resnet-18 (He et al., 2016). |
| Dataset Splits | No | The paper mentions using the Cifar-10 dataset and refers to training loss and test accuracy, but it does not explicitly provide details about training/validation/test splits or how data was partitioned for validation purposes. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Resnet-18 and SGD, but it does not specify version numbers for any software dependencies or libraries needed to replicate the experiments. |
| Experiment Setup | Yes | The parameter M is tuned by a grid search in {100, 101, 102, 103, 104} for all methods, and the momentum parameter γ is tuned by a grid search in {10 1, 10 2, 10 3, 10 4, 10 5}. We set the maximum number of iterations to be 104, and the tolerance is 0.02. We use a batch size of 256 and run 300 epochs. We use the standard step size parameter M = 10 (corresponding to a learning rate of 0.1) for the experiment. |