2018/05/18 by Sebastian Claici, Claici, Sebastian, Aude Genevay +3
Computer Science · Mathematics · #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Image Segmentation Techniques #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1805.07412
openalex publication_date 2018/05/18 · arxiv created 2020/03/02 · arxiv updated 2020/03/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The proliferation of large data sets and Bayesian inference techniques motivates demand for better data sparsification. Coresets provide a principled way of summarizing a large dataset via a smaller one that is guaranteed to match the performance of the full data set on specific problems. Classical coresets, however, neglect the underlying data distribution, which is often continuous. We address this oversight by introducing Wasserstein measure coresets, an extension of coresets which by definition takes into account generalization. Our formulation of the problem, which essentially consists in minimizing the Wasserstein distance, is solvable via stochastic gradient descent. This yields an algorithm which simply requires sample access to the data distribution and is able to handle large data streams in an online manner. We validate our construction for inference and clustering.