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This paper introduces Quantile AlignTree Flow Matching (QAT-FM), a structured coupling method for flow matching that constructs hierarchical couplings using quantile-aligned trees to achieve non-crossing paths and efficient training for high-dimensional generative tasks.
FastAlign presents a scalable, sparsity-aware framework for optimal transport-based network alignment, achieving state-of-the-art accuracy while reducing runtime by up to 9.45x on CPU and 32.54x on GPU.
Introduces FewRS, a resampling-based approach that drastically reduces the number of resampled datasets required for statistically-sound data mining, achieving up to two orders of magnitude speedup while maintaining rigorous false discovery control and high statistical power.