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..
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances
Authors: Ruben Ohana, Kimia Nadjahi, Alain Rakotomamonjy, Liva Ralaivola
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide three types of results: i) PAC-Bayesian generalization bounds that hold on what we refer as adaptive Sliced Wasserstein distances, i.e. SW defined with respect to arbitrary distributions of slices (among which data-dependent distributions), ii) a principled procedure to learn the distribution of slices that yields maximally discriminative SW, by optimizing our theoretical bounds, and iii) empirical illustrations of our theoretical findings. |
| Researcher Affiliation | Collaboration | 1Flatiron Institute, USA 2MIT, USA 3Criteo AI Lab, France. |
| Pseudocode | Yes | Algorithm 1 PAC-SW: Adaptive SW via PAC-Bayes bound optimization. ... Algorithm A2 PAC-Bayes bound optimization for v MF-based SW |
| Open Source Code | Yes | All our numerical experiments presented in Section 5 can be reproduced using the code we provided in https://github.com/rubenohana/PACBayesian_Sliced-Wasserstein. |
| Open Datasets | Yes | We consider a generative modeling task on MNIST data (Deng, 2012) |
| Dataset Splits | No | The paper mentions training and test data, but does not explicitly describe a validation split or its methodology. |
| Hardware Specification | Yes | Timing results of this experiment were obtained with a NVIDIA GPU A100 80 GB, compared to Figure 4 which was on a NVIDIA V100. |
| Software Dependencies | No | The paper mentions using Adam (Kingma & Ba, 2015) with its default parameters, but no specific version numbers for software libraries or dependencies are provided. |
| Experiment Setup | Yes | We sample n = 500 samples from µ and ν and optimize ρ (µn, νn): the optimization is performed on the space of v MF distributions, using Adam (Kingma & Ba, 2015) with its default parameters. ... For each minibatch of size 512, the distribution ρ is learned by optimizing 100 projections over 100 iterations and the generative model is trained over 400 epochs. |