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..
Stochastic Optimization for Regularized Wasserstein Estimators
Authors: Marin Ballu, Quentin Berthet, Francis Bach
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide theoretical guarantees and illustrate the performance of our algorithm with experiments on synthetic data. We demonstrate the performance of the algorithm on simulated experiments. |
| Researcher Affiliation | Collaboration | 1Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, United Kingdom 2Google Research, Brain Team, Paris, France 3Inria, ENS, PSL Research University, Paris, France. |
| Pseudocode | Yes | Algorithm 1 SGD for Wasserstein estimator |
| Open Source Code | No | The paper does not contain a statement or link indicating the release of source code for the described methodology. |
| Open Datasets | No | The paper uses synthetic data, stating, "We generate X and Y randomly by drawing two sets of independent Gaussian vectors of respective sizes I and J." However, it does not provide access information (e.g., a link or citation) for this data or the generation process. |
| Dataset Splits | No | The paper describes simulations with synthetic data but does not specify explicit training, validation, or test splits for this data. |
| Hardware Specification | No | The paper does not contain any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies (e.g., libraries, frameworks, or solvers) along with their version numbers. |
| Experiment Setup | Yes | Here ε = 0.01, η = 0.02, with the same problem is the same as in the experiments on the regularization term shown in Figure 1. |