Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Algorithms for Sum-Of-Minimum Optimization
Authors: Lisang Ding, Ziang Chen, Xinshang Wang, Wotao Yin
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The efficiency of our algorithms is numerically examined on multiple tasks, including generalized principal component analysis, mixed linear regression, and small-scale neural network training. |
| Researcher Affiliation | Collaboration | 1Department of Mathematics, University of California, Los Angeles, Los Angeles, CA, USA 2Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA 3Decision Intelligence Lab, Alibaba US, Bellevue, WA, USA. |
| Pseudocode | Yes | The pseudo-code of this algorithm is shown in Algorithm 1. |
| Open Source Code | Yes | Our code with documentation can be found at https://github.com/Lisang Ding/ Sum-of-Minimum_Optimization. |
| Open Datasets | No | The dataset {(ai, bi)}N i=1 for the ℓ2-regularized mixed linear regression is synthetically generated in the following way:... |
| Dataset Splits | No | No specific dataset split information (e.g., percentages, sample counts for training, validation, and test sets) or mention of a validation set was provided. The paper states: "In our experiments, the training dataset size is N = 1000 and the testing dataset size is 200." |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running experiments are provided in the paper. |
| Software Dependencies | No | Software components like "ADAM optimizer" are mentioned, but no specific version numbers are provided to ensure reproducibility. |
| Experiment Setup | Yes | We set the maximum iteration number as 50 for Algorithm 2 with (5) and terminate the algorithm once the objective function stops decreasing... We use the default ADAM learning rate γ = 1e 3. We set r = 10 in Lloyd s Algorithm 2 and fix the cluster number k = 5. |