Statistical Learning

Sampling Without Compromising Accuracy in Adaptive Data Analysis ALT 2020 Scalable Approximate MCMC Algorithms for the Horseshoe Prior JMLR 2020 Scalar vs. mereological conceptualizations of the N-BY-N and NUM-BY-NUM adverbials COLING 2020 Scaling Hidden Markov Language Models EMNLP 2020 Second Order PAC-Bayesian Bounds for the Weighted Majority Vote NeurIPS 2020 Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components AISTATS 2020 Semi-parametric Learning of Structured Temporal Point Processes JMLR 2020 Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful UAI 2020 Sequential change-point detection in high-dimensional Gaussian graphical models JMLR 2020 Sequential Mode Estimation with Oracle Queries AAAI 2020 Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models AISTATS 2020 Sharp Asymptotics and Optimal Performance for Inference in Binary Models AISTATS 2020 Sharper Bounds for Uniformly Stable Algorithms COLT 2020 Sharper Generalization Bounds for Pairwise Learning NeurIPS 2020 Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification ICML 2020 Sharp uniform convergence bounds through empirical centralization NeurIPS 2020 Showing Your Work Doesn’t Always Work ACL 2020 Significance Tests for Neural Networks JMLR 2020 Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching JMLR 2020 Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients JMLR 2020 Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms JMLR 2020 Solving General Elliptical Mixture Models through an Approximate Wasserstein Manifold AAAI 2020 Speakers Fill Lexical Semantic Gaps with Context EMNLP 2020 Spectral Deconfounding via Perturbed Sparse Linear Models JMLR 2020 Spectral Methods for Ranking with Scarce Data UAI 2020