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Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition. (arXiv:2103.09947v1 [cs.LG]) arxiv.org/abs/2103.09947

Outcome-guided Sparse K-means for Disease Subtype Discovery via Integrating Phenotypic Data with High-dimensional Transcriptomic Data. (arXiv:2103.09974v1 [q-bio.QM]) arxiv.org/abs/2103.09974

Linear Iterative Feature Embedding: An Ensemble Framework for Interpretable Model. (arXiv:2103.09983v1 [stat.ML]) arxiv.org/abs/2103.09983

Learning Time Series from Scale Information. (arXiv:2103.10026v1 [cond-mat.mes-hall]) arxiv.org/abs/2103.10026

Probabilistic Simplex Component Analysis. (arXiv:2103.10027v1 [eess.SP]) arxiv.org/abs/2103.10027

Bartlett correction of an independence test in a multivariate Poisson model. (arXiv:2103.10058v1 [math.ST]) arxiv.org/abs/2103.10058

Sequential Estimation of Convex Divergences using Reverse Submartingales and Exchangeable Filtrations. (arXiv:2103.09267v1 [math.ST]) arxiv.org/abs/2103.09267

Self-Validated Ensemble Models for Design of Experiments. (arXiv:2103.09303v1 [stat.ME]) arxiv.org/abs/2103.09303

Optimal stratification of survival data via Bayesian nonparametric mixtures. (arXiv:2103.09305v1 [stat.ME]) arxiv.org/abs/2103.09305

A Classical Search Game in Discrete Locations. (arXiv:2103.09310v1 [stat.ML]) arxiv.org/abs/2103.09310

Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison. (arXiv:2103.09316v1 [cs.LG]) arxiv.org/abs/2103.09316

A Note on Over- and Under-Representation Among Populations with Normally-Distributed Traits. (arXiv:2103.09324v1 [math.PR]) arxiv.org/abs/2103.09324

The planted matching problem: Sharp threshold and infinite-order phase transition. (arXiv:2103.09383v1 [math.ST]) arxiv.org/abs/2103.09383

Simultaneous Decorrelation of Matrix Time Series. (arXiv:2103.09411v1 [stat.ME]) arxiv.org/abs/2103.09411

A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models. (arXiv:2103.08594v1 [cs.LG]) arxiv.org/abs/2103.08594

Estimation of parameters of the Gumbel type-II distribution under AT-II PHCS with an application of Covid-19 data. (arXiv:2103.08641v1 [stat.ME]) arxiv.org/abs/2103.08641

Function approximation by deep neural networks with parameters $\{0,\pm \frac{1}{2}, \pm 1, 2\}$. (arXiv:2103.08659v1 [stat.ML]) arxiv.org/abs/2103.08659

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