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Sketched Clustering via Hybrid Approximate Message Passing.

, , , and . IEEE Trans. Signal Process., 67 (17): 4556-4569 (2019)

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Sketching Data Sets for Large-Scale Learning: Keeping only what you need., , , , , and . IEEE Signal Process. Mag., 38 (5): 12-36 (2021)Mean Nyström Embeddings for Adaptive Compressive Learning., , , and . AISTATS, volume 151 of Proceedings of Machine Learning Research, page 9869-9889. PMLR, (2022)Sketched Clustering via Hybrid Approximate Message Passing., , , and . IEEE Trans. Signal Process., 67 (17): 4556-4569 (2019)Mean Nyström Embeddings for Adaptive Compressive Learning., , , and . CoRR, (2021)Efficient and Privacy-Preserving Compressive Learning. (Méthodes efficaces pour l'apprentissage compressif avec garanties de confidentialité).. University of Rennes 1, France, (2020)Differentially Private Compressive K-means., , , , , and . ICASSP, page 7933-7937. IEEE, (2019)Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees., , , , and . CoRL, volume 229 of Proceedings of Machine Learning Research, page 3010-3029. PMLR, (2023)Sketching Datasets for Large-Scale Learning (long version)., , , , , and . CoRR, (2020)M2M: A General Method to Perform Various Data Analysis Tasks from a Differentially Private Sketch., , , , and . STM, volume 13867 of Lecture Notes in Computer Science, page 117-135. Springer, (2022)Nyström Kernel Mean Embeddings., , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 3006-3024. PMLR, (2022)