Stochastic Optimization for Regularized Wasserstein Estimators
Authors: Marin Ballu, Quentin Berthet, Francis Bach
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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. |