arXiv Statistics @arxiv_stats@qoto.org

Demystifying and Generalizing BinaryConnect. (arXiv:2110.13220v1 [cs.LG]) http://arxiv.org/abs/2110.13220

Prediction-focused Mixture Models. (arXiv:2110.13221v1 [cs.LG]) http://arxiv.org/abs/2110.13221

A Constructive Proof of the Glivenko-Cantelli Theorem. (arXiv:2110.13236v1 [math.PR]) http://arxiv.org/abs/2110.13236

Integrative Clustering of Multi-View Data by Nonnegative Matrix Factorization. (arXiv:2110.13240v1 [stat.ML]) http://arxiv.org/abs/2110.13240

Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust. (arXiv:2110.13262v1 [econ.EM]) http://arxiv.org/abs/2110.13262

Scalable Bayesian divergence time estimation with ratio transformations. (arXiv:2110.13298v1 [q-bio.PE]) http://arxiv.org/abs/2110.13298

Robust Learning of Physics Informed Neural Networks. (arXiv:2110.13330v1 [cs.LG]) http://arxiv.org/abs/2110.13330

Imprecise Subset Simulation. (arXiv:2110.11955v1 [stat.ME]) http://arxiv.org/abs/2110.11955

Fairness in Missing Data Imputation. (arXiv:2110.12002v1 [cs.LG]) http://arxiv.org/abs/2110.12002

A Prototype-Oriented Framework for Unsupervised Domain Adaptation. (arXiv:2110.12024v1 [cs.LG]) http://arxiv.org/abs/2110.12024

Uncertainty Quantification For Low-Rank Matrix Completion With Heterogeneous and Sub-Exponential Noise. (arXiv:2110.12046v1 [stat.ML]) http://arxiv.org/abs/2110.12046

A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses for Administrative Tax Data. (arXiv:2110.12055v1 [stat.AP]) http://arxiv.org/abs/2110.12055

The Causal Loss: Driving Correlation to Imply Causation. (arXiv:2110.12066v1 [cs.LG]) http://arxiv.org/abs/2110.12066

Gaussian Graphical Model Selection for Huge Data via Minipatch Learning. (arXiv:2110.12067v1 [stat.ML]) http://arxiv.org/abs/2110.12067

Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds. (arXiv:2110.12087v1 [cs.LG]) http://arxiv.org/abs/2110.12087

Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. (arXiv:2110.12088v1 [cs.LG]) http://arxiv.org/abs/2110.12088

Why Machine Learning Cannot Ignore Maximum Likelihood Estimation. (arXiv:2110.12112v1 [math.ST]) http://arxiv.org/abs/2110.12112

Combining Parametric and Nonparametric Models to Estimate Treatment Effects in Observational Studies. (arXiv:2110.11349v1 [stat.ME]) http://arxiv.org/abs/2110.11349

SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning. (arXiv:2110.11395v1 [cs.LG]) http://arxiv.org/abs/2110.11395

Towards Noise-adaptive, Problem-adaptive Stochastic Gradient Descent. (arXiv:2110.11442v1 [math.OC]) http://arxiv.org/abs/2110.11442