W-SparQ-BL models time-varying lower-level responses with multi-output GPs and sparse approximations to achieve sublinear dynamic regret in bilevel optimization under noise.
Advances in Neural Information Processing Systems , volume=
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FastUMAP approximates UMAP via sparse bipartite point-landmark graphs and Nystrom initialization to deliver lower runtimes than Barnes-Hut t-SNE on most tested datasets while retaining competitive kNN accuracy.
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FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Sampling
FastUMAP approximates UMAP via sparse bipartite point-landmark graphs and Nystrom initialization to deliver lower runtimes than Barnes-Hut t-SNE on most tested datasets while retaining competitive kNN accuracy.