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.
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 | Input Tokens: 21,108 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,577 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 | 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. |