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..
Progressive Entropic Optimal Transport Solvers
Authors: Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein, James Thornton, Jonathan Niles-Weed, Marco Cuturi
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run experiments to evaluate the performance of PROGOT across various datasets, on its ability to act as a map estimator, and to produce couplings between the source and target points. We also prove the statistical consistency of PROGOT when estimating OT maps. |
| Researcher Affiliation | Collaboration | Parnian Kassraie ETH Zurich, Apple EMAIL Aram-Alexandre Pooladian New York University EMAIL Michal Klein Apple EMAIL James Thornton Apple EMAIL Jonathan Niles-Weed New York University EMAIL Marco Cuturi Apple EMAIL |
| Pseudocode | Yes | Algorithm 1 SINK(a, X, b, Y, ε, τ, finit, ginit). Algorithm 2 PROGOT(a, X, b, Y, (εk, αk, τk)k) Algorithm 3 TProg[b, Y, (g(k), εk, αk)k] |
| Open Source Code | Yes | The code for PROGOT, is included in the OTT-JAX package [Cuturi et al., 2022b]. We include the base implementation of our main algorithm in Jax as supplementary material. |
| Open Datasets | Yes | We consider the sci-Plex single-cell RNA sequencing data from [Srivatsan et al., 2020] We consider the entire grayscale CIFAR10 dataset [Krizhevsky et al., 2009] single-cell multiplex data of Bunne et al. [2023] Gaussian Mixture data, using the dataset of Korotin et al. [2021] |
| Dataset Splits | Yes | To choose ε for entropic estimators, we split the training data to get an evaluation set and perform 5-fold cross-validation on the grid of {2 3, . . . , 23} ε0 |
| Hardware Specification | Yes | Experiments were run on a single Nvidia A100 GPU for a total of 24 hours. Smaller experiments and debugging was performed on a single Mac Book M2 Max. |
| Software Dependencies | No | The paper mentions 'JAX' and 'OTT-JAX' as the framework where the code is implemented and available. However, it does not specify explicit version numbers for these software dependencies or other key libraries used. |
| Experiment Setup | Yes | In map experiments, unless mentioned otherwise, we run PROGOT for K = 16 steps, with a constant-speed schedule for αk, and the regularization schedule set via Algorithm 4 with β0 = 5 and sp {2 3, . . . , 23}. We choose the number of hidden layers for both as [128, 64, 64]. For the ICNN we use a learning rate η = 10 3, batch size b = 256 and train it using the Adam optimizer [Kingma and Ba, 2014] for 2000 iterations. For the Monge Gap we set the regularization constant λMG = 10, λcons = 0.1 and the Sinkhorn regularization to ε = 0.01. We train the Monge Gap in a similar setting, except that we set η = 0.01. |