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..
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections
Authors: Kimia Nadjahi, Alain Durmus, Pierre E Jacob, Roland Badeau, Umut Simsekli
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical findings on synthetic datasets, and illustrate the proposed approximation on a generative modeling problem. |
| Researcher Affiliation | Academia | Kimia Nadjahi1 , Alain Durmus2, Pierre E. Jacob3, Roland Badeau1, Umut Sim sekli4 1: LTCI, Télécom Paris, Institut Polytechnique de Paris, France 2: Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-91190 Gif-sur-Yvette, France 3: Department of Information Systems, Decision Sciences and Statistics, ESSEC Business School, Cergy, France 4: INRIA Département d Informatique de l École Normale Supérieure, PSL Research University, Paris, France |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our empirical results can be reproduced with our open source code2. 2See https://github.com/kimiandj/fast_sw |
| Open Datasets | Yes | models on MNIST and Celeb A |
| Dataset Splits | Yes | In each setting, we generate two sets of d-dimensional samples, denoted by {x(j)}n j=1 and {y(j)}n j=1 with n = 104 |
| Hardware Specification | No | The paper mentions that models were trained 'On GPU' and 'on CPU' and refers to 'GPU-accelerated implementation', but does not provide specific hardware details such as exact GPU/CPU models or processor types. |
| Software Dependencies | No | The paper states 'the models were trained using Py Torch', but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | In each setting, we generate two sets of d-dimensional samples, denoted by {x(j)}n j=1 and {y(j)}n j=1 with n = 104 |