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Unsupervised group matching with application to cross-lingual topic matching without alignment information.

, , , and . Data Min. Knowl. Discov., 31 (2): 350-370 (2017)

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Monte Carlo Filtering Using Kernel Embedding of Distributions., , , and . AAAI, page 1897-1903. AAAI Press, (2014)Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings., , and . CoRR, (2017)Convergence guarantees for kernel-based quadrature rules in misspecified settings., , and . NIPS, page 3288-3296. (2016)Improved Random Features for Dot Product Kernels., , and . J. Mach. Learn. Res., (2024)Recovering Distributions from Gaussian RKHS Embeddings., and . AISTATS, volume 33 of JMLR Workshop and Conference Proceedings, page 457-465. JMLR.org, (2014)Simulator Calibration under Covariate Shift with Kernels., , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 1244-1253. PMLR, (2020)Kernel Recursive ABC: Point Estimation with Intractable Likelihood., , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 2405-2414. PMLR, (2018)Unsupervised group matching with application to cross-lingual topic matching without alignment information., , , and . Data Min. Knowl. Discov., 31 (2): 350-370 (2017)Convergence Guarantees for Adaptive Bayesian Quadrature Methods., and . NeurIPS, page 6234-6245. (2019)Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes., , and . CoRR, (2021)