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Deep Ensembles Work, But Are They Necessary?. (arXiv:2202.06985v1 [cs.LG]) arxiv.org/abs/2202.06985

Unlabeled Data Help: Minimax Analysis and Adversarial Robustness. (arXiv:2202.06996v1 [stat.ML]) arxiv.org/abs/2202.06996

Privacy-preserving estimation of an optimal individualized treatment rule : A case study in maximizing time to severe depression-related outcomes. (arXiv:2202.07003v1 [stat.AP]) arxiv.org/abs/2202.07003

Statistical inference for intrinsic wavelet estimators of SPD matrices in a log-Euclidean manifold. (arXiv:2202.07010v1 [stat.ME]) arxiv.org/abs/2202.07010

Statistical estimation of spatial wave extremes for tropical cyclones from small data samples: validation of the STM-E approach using long-term synthetic cyclone data for the Caribbean Sea. (arXiv:2202.07045v1 [stat.AP]) arxiv.org/abs/2202.07045

Introducing the ICBe Dataset: Very High Recall and Precision Event Extraction from Narratives about International Crises. (arXiv:2202.07081v1 [stat.AP]) arxiv.org/abs/2202.07081

Statistical Inference After Adaptive Sampling in Non-Markovian Environments. (arXiv:2202.07098v1 [cs.LG]) arxiv.org/abs/2202.07098

Group testing via residuation and partial geometries. (arXiv:2202.05876v1 [stat.OT]) arxiv.org/abs/2202.05876

Statistical Limits for Testing Correlation of Hypergraphs. (arXiv:2202.05888v1 [math.ST]) arxiv.org/abs/2202.05888

Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness. (arXiv:2202.05920v1 [cs.LG]) arxiv.org/abs/2202.05920

Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data. (arXiv:2202.05928v1 [cs.LG]) arxiv.org/abs/2202.05928

POT-flavored estimator of Pickands dependence function. (arXiv:2202.05935v1 [stat.ME]) arxiv.org/abs/2202.05935

Private Adaptive Optimization with Side Information. (arXiv:2202.05963v1 [cs.LG]) arxiv.org/abs/2202.05963

scpi: Uncertainty Quantification for Synthetic Control Estimators. (arXiv:2202.05984v1 [stat.ME]) arxiv.org/abs/2202.05984

Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning. (arXiv:2202.06052v1 [cs.LG]) arxiv.org/abs/2202.06052

Relaxing the Feature Covariance Assumption: Time-Variant Bounds for Benign Overfitting in Linear Regression. (arXiv:2202.06054v1 [cs.LG]) arxiv.org/abs/2202.06054

Depth profiles and the geometric exploration of random objects through optimal transport. (arXiv:2202.06117v1 [stat.ME]) arxiv.org/abs/2202.06117

A Field of Experts Prior for Adapting Neural Networks at Test Time. (arXiv:2202.05271v1 [cs.CV]) arxiv.org/abs/2202.05271

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