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].
Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances
Authors: Sloan Nietert, Ziv Goldfeld, Ritwik Sadhu, Kengo Kato
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theory is validated by numerical experiments, which altogether provide a comprehensive quantitative account of the scalability question. and 6 Empirical Results |
| Researcher Affiliation | Academia | Sloan Nietert Cornell University EMAIL Ritwik Sadhu Cornell University EMAIL Ziv Goldfeld Cornell University EMAIL Kengo Kato Cornell University EMAIL |
| Pseudocode | Yes | Algorithm 1 Projected subgradient method for w2 2 |
| Open Source Code | Yes | The code for all experiments and figures is publicly available at https://github.com/swnietert/SWD_guarantees. |
| Open Datasets | No | Our experiments use only synthetic data. |
| Dataset Splits | No | The paper describes how samples are generated for experiments (e.g., 'n = 500' or 'n = 10dϵ^2 samples'), but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | Yes | All computations were performed on a single machine with an Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz CPU, 64 GB of RAM, and an NVIDIA GeForce RTX 3090 GPU. |
| Software Dependencies | No | The paper states 'All code was written in Python 3.9 and relies on the NumPy, SciPy, Matplotlib, and scikit-learn libraries', but only Python has a version number specified. Other library versions are not provided. |
| Experiment Setup | Yes | Sample size is fixed at n = 500 and computation times are averaged over 10 trials. and For d {10, 20, . . . , 200}, we take n = 10dϵ 2 samples, with (1 ϵ)n drawn i.i.d. from N(0, Id) and ϵn from a product noise distribution used in [19], with ϵ = 0.1. |