Self-supervised Metric Learning in Multi-View Data: A Downstream Task Perspective
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Publication:6144761
DOI10.1080/01621459.2022.2057317arXiv2106.07138OpenAlexW3172311569MaRDI QIDQ6144761
Publication date: 8 January 2024
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2106.07138
Cites Work
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- Equivalence of distance-based and RKHS-based statistics in hypothesis testing
- Generalization bounds for metric and similarity learning
- Fast generalization rates for distance metric learning. Improved theoretical analysis for smooth strongly convex distance metric learning
- A new test for multivariate normality
- Optimal weighted nearest neighbour classifiers
- Central limit theorem for integrated square error of multivariate nonparametric density estimators
- Lectures on the nearest neighbor method
- Fast learning rates for plug-in classifiers
- Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests
- A two-sample test for high-dimensional data with applications to gene-set testing
- On the optimality of sliced inverse regression in high dimensions
- Metric Learning: A Survey
- Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties
- Sufficient dimension reduction and prediction in regression
- Sliced Inverse Regression for Dimension Reduction
- Constructing Summary Statistics for Approximate Bayesian Computation: Semi-Automatic Approximate Bayesian Computation
- Spectral Methods for Data Science: A Statistical Perspective
- Statistical Foundations of Data Science
- Metric Learning
- Large Sample Covariance Matrices and High-Dimensional Data Analysis
- Hypothesis testing for phylogenetic composition: a minimum-cost flow perspective
- Query-Augmented Active Metric Learning
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