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..
Learning Elastic Costs to Shape Monge Displacements
Authors: Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugene Ndiaye, Jonathan Niles-Weed, Marco Cuturi
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the soundness of our procedure on synthetic data, generated using our first contribution, in which we show near-perfect recovery of A s subspace using only samples. We demonstrate the applicability of this method by showing predictive improvements on single-cell data tasks. |
| Researcher Affiliation | Collaboration | Michal Klein Apple EMAIL Aram-Alexandre Pooladian NYU EMAIL Pierre Ablin Apple EMAIL Eugène Ndiaye Apple EMAIL Jonathan Niles-Weed NYU EMAIL Marco Cuturi Apple EMAIL |
| Pseudocode | Yes | Algorithm 1 MBO-ESTIMATOR(X, Y; γ, τ, ε) ... Algorithm 2 GROUND-TRUTH OT MAP T h g ... Algorithm 3 RECOVER-THETA: (X, Y; γ, θ0) |
| Open Source Code | No | We will release the entire codebase for experiments in coming weeks, as python notebooks/tutorials. |
| Open Datasets | Yes | using single-cell RNA sequencing data from [Srivatsan et al., 2020]. |
| Dataset Splits | Yes | We then use 80% train/20% test folds to benchmark two MBO estimators |
| Hardware Specification | No | Although no claim is made in terms of compute performance, the fairly small scale of the experiments allows to execute these runs on a single GPU. |
| Software Dependencies | No | In practice, we use the JAXOPT [Blondel et al., 2021] library to run proximal gradient descent. ... Our code implements a parameterized Reg TICost class, added to OTT-JAX [Cuturi et al., 2022]. ... We plot the Sinkhorn divergence (cf. Feydy et al. [2019]) for the ℓ2 2 cost for reference (see the documentation in OTT-JAX [Cuturi et al., 2022]). |
| Experiment Setup | Yes | We report performance after 1000 iterations of Riemannian gradient descent, with a step-size η of 0.1/ i + 1 at iteration i. |