Statistical Learning
SeeA: Efficient Exploration-Enhanced A Search by Selective Sampling
Neural Information Processing Systems
Three sampling techniques are presented for comparative investigations.
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MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
Neural Information Processing Systems
The fundamental problem of non-parametric two-sample testing consists in detecting the difference between any two distributions having access only to samples from these.
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Adversarial Training from Mean Field Perspective
Neural Information Processing Systems
We also show that the network width alleviates these issues.
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GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics
Neural Information Processing Systems
Single-cell genomics has significantly advanced our understanding of cellular behavior, catalyzing innovations in treatments and precision medicine.
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