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.
Stochastic Optimization with Arbitrary Recurrent Data Sampling
Authors: William Powell, Hanbaek Lyu
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 40,284 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,257 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 experimentally validate our results for the tasks of non-negative matrix factorization and logistic regression. We find that our method is robust to data heterogeneity as it produces stable iterate trajectories while still maintaining fast convergence (see Sec. 4.2). |
| Researcher Affiliation | Academia | 1Department of Mathematics, University of Wisconsin-Madison, WI, USA. |
| Pseudocode | Yes | Algorithm 1 Incremental Majorization Minimization with Dynamic Proximal Regularization... Algorithm 2 Incremental Majorization Minimization with Diminishing Radius |
| Open Source Code | No | The paper does not provide concrete access to its own source code for the methodology described. |
| Open Datasets | Yes | We consider a randomly drawn collection of 5000 images from the MNIST (Deng, 2012) dataset |
| Dataset Splits | No | The paper describes how the dataset was structured for the experiments (e.g., divided into groups, batched into nodes) but does not provide explicit training, validation, and test dataset split percentages or counts needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) 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) needed to replicate the experiment. |
| Experiment Setup | Yes | We include here a list of hyperparamters used for the NMF experiments. For Ada Grad we used constant step size parameter η = 0.5. For both RMISO-DPR and RMISO-CPR we set ρ = 2500 for the random walk and ρ = 50 for cyclic sampling. For the diminishing radius version RMISO-DR we set rn = 1 n log(n+1). ... The hyperparameters for the logistic regression experiments were chosen as follows. For MCSAG and RMISO/MISO we took L = 2/5. ... We ran SGD with a decaying step size of the form αn = α nγ where α = 0.1 and γ = 0.5. For SGD-HB and Ada Grad we used step sizes α = 0.05 and SGD-HB momentum parameter β = 0.9. |