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The K-sample testing problem involves determining whether K groups of data points are each drawn from the same distribution.
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Distance covariance in metric spaces
Lyons, R., 2013 · 2013
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Sejdinovic, D., Sriperumbudur, B., Gretton, A., Fukumizu, K., 2013 · 2013
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Network dependence testing via diffusion maps and distance-based correlations
Lee, Y., Shen, C., Priebe, C.E., Vogelstein, J.T., 2019 · 2019
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Discovering and deciphering relationships across disparate data modalities
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Ball covariance: A generic measure of dependence in banach space
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From distance correlation to multiscale graph correlation
Shen, C., Priebe, C.E., Vogelstein, J.T., 2020 · 2020
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The exact equivalence of distance and kernel methods in hypothesis testing
Shen, C., Vogelstein, J.T., 2021 · 2021
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A regression perspective on generalized distance covariance and the hilbert–schmidt independence criterion
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Conditional Distance Correlation
Wang, X., Pan, W., Hu, W., Tian, Y., Zhang, H., 2015 · 2015
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A test of relative similarity for model selection in generative models, in: International Conference on Learning Representations
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Consistent distribution-free k-sample and independence tests for univariate random variables
Heller, R., Heller, Y., Kaufman, S., Brill, B., Gorfine, M., 2016 · 2016
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Learning interpretable characteristic kernels via decision forests
Panda, S., Shen, C., Vogelstein, J.T., 2024b
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Edelmann, D., Goeman, J., 2022 · 2022
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The chi-square test of distance correlation
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One-hot graph encoder embedding
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Rank-based indices for testing independence between two high-dimensional vectors
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Kernel methods for measuring independence
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