Asymptotics of smoothed Wasserstein distances in the small noise regime
Authors: Yunzi Ding, Jonathan Niles-Weed
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For each of these GOT tasks, we draw 200 samples from the source distribution µk Nσ and target distribution ν Nσ, and use the empirical W2 distance as an estimate of the true W2(µk Nσ, ν Nσ). We repeat the process 100 times and report the mean, as shown in the following figures. |
| Researcher Affiliation | Academia | 1Courant Institute of Mathematical Sciences, NYU 2Courant Institute of Mathematical Sciences and the Center for Data Science, NYU |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] |
| Open Datasets | No | The paper describes drawing samples for numerical examples but does not mention using or providing access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes drawing samples for numerical examples but does not specify explicit training/validation/test dataset splits. |
| 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, such as library names with version numbers, used to replicate the experiments. |
| Experiment Setup | No | The numerical example describes the number of samples drawn and repetitions, but it does not provide specific hyperparameters or system-level training settings as would be found in a machine learning experiment. |