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..
Mapping Estimation for Discrete Optimal Transport
Authors: Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show the interest and the relevance of our method in two tasks: domain adaptation and image editing. The paper includes a dedicated section '4 Experiments' with detailed tables (Table 1 and Table 2) showing accuracy results on Moons and Office-Caltech datasets, and Figure 2 illustrating image editing results, all of which are empirical evaluations. |
| Researcher Affiliation | Academia | Micha el Perrot Univ Lyon, UJM-Saint-Etienne, CNRS, Lab. Hubert Curien UMR 5516, F-42023 EMAIL Nicolas Courty Universit e de Bretagne Sud, IRISA, UMR 6074, CNRS, EMAIL R emi Flamary Universit e Cˆote d Azur, Lagrange, UMR 7293 , CNRS, OCA EMAIL Amaury Habrard Univ Lyon, UJM-Saint-Etienne, CNRS, Lab. Hubert Curien UMR 5516, F-42023 EMAIL |
| Pseudocode | Yes | Algorithm 1: Joint Learning of L and γ. input :Xs, Xt source and target examples and λγ, λT hyper parameters. output:L, γ. 2 Initialize k = 0, γ0 Π and L0 = I 4 Learn γk+1 solving problem (6) with fixed Lk using a Frank-Wolfe approach. 5 Learn Lk+1 using Equation (9), (12) or their biased counterparts with fixed γk+1. 6 Set k = k + 1. 7 until convergence |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for their proposed methodology. |
| Open Datasets | Yes | We consider two domain adaptation (DA) datasets, namely Moons [21] and Office Caltech [22]. These datasets are standard and cited with their respective papers [21] and [22], confirming their public availability. |
| Dataset Splits | Yes | For the Moons dataset, 'we consider 300 source and target examples for training'. For the Office-Caltech dataset, 'During the training process we consider all the examples from the source domain and half of the examples from the target domain'. The paper also states: 'All the hyper-parameters are tuned according to a grid search on the source and target training instances using a circular validation procedure derived from [21, 25] and described in the supplementary material.' |
| Hardware Specification | No | The paper states that 'each example is computed in less than 30s on a standard personal laptop.' This description lacks specific details such as CPU, GPU, or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies, libraries, or their version numbers used for the implementation or experiments. |
| Experiment Setup | Yes | For GFK and SA we choose the dimension of the subspace d {3, 6, . . . , 30}, for L1L2 and OTE we set the parameter for entropy regularization in {10 6, 10 5, . . . , 105}, for L1L2 we choose the class related parameter η {10 5, 10 4, . . . , 102}, for all our methods we choose λT , λγ {10 3, 10 2, . . . , 100}. For the image editing task, specific λT and λγ values are provided: '(λT = 10 2, λT = 103 for respectively the linear and kernel versions, and λγ = 10 7 for both cases).' |