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 [1].
Safe and Sparse Newton Method for Entropic-Regularized Optimal Transport
Authors: Zihao Tang, Yixuan Qiu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Various numerical experiments are conducted to demonstrate the effectiveness of the proposed algorithm in solving large-scale OT problems. In this section, we test the performance of the proposed SSNS algorithm on various numerical experiments. |
| Researcher Affiliation | Academia | Zihao Tang School of Statistics and Data Science Shanghai University of Finance and Economics EMAIL Yixuan Qiu School of Statistics and Data Science Shanghai University of Finance and Economics EMAIL |
| Pseudocode | Yes | Algorithm 1 Sparsifying the Hessian matrix. |
| Open Source Code | Yes | An efficient implementation is included in the Reg OT Python package2. 2https://github.com/yixuan/regot-python. |
| Open Datasets | Yes | (Fashion-)MNIST: OT between a pair of images from the MNIST [15] or Fashion-MNIST [37] dataset. Image Net: OT between two categories of images from the Image Net dataset [9]. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology for training, validation, and test sets) was found. The paper describes problem sizes and data construction but not data partitioning for model training/evaluation. |
| Hardware Specification | Yes | All experiments in this article are conducted on a personal computer with an Intel i9-13900K CPU, 32 GB memory, and a Ubuntu 24.10 operating system. |
| Software Dependencies | No | The paper mentions 'Reg OT Python package' and 'Ubuntu 24.10 operating system' but does not specify version numbers for other critical software dependencies (e.g., Python, PyTorch, or other libraries). |
| Experiment Setup | Yes | For entropic-regularized OT, a commonly-used criterion to evaluate optimality is the marginal error of the estimated transport plan... Then we consider two settings of the regularization parameter, η = 0.01 and η = 0.001. Default values: µ0 = 1, ν0 = 0.01, cl = 0.1, cu = 1, κ = 0.001, γ = 1, ρ0 = 1/4 setting (ξ[0], ξ[1], ξ[2], ξ[3]) = (1, 0.5, 0.25, 0.1) gives reasonably good performance in most numerical experiments. |