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Statistical Learning

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Papers

A Linear-time Independence Criterion Based on a Finite Basis Approximation AISTATS 2020
A Characterization of Mean Squared Error for Estimator with Bagging AISTATS 2020
A Stein Goodness-of-fit Test for Directional Distributions AISTATS 2020
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data AISTATS 2020
A Unified Statistically Efficient Estimation Framework for Unnormalized Models AISTATS 2020
Imputation estimators for unnormalized models with missing data AISTATS 2020
Obfuscation via Information Density Estimation AISTATS 2020
Contextual Online False Discovery Rate Control AISTATS 2020
Kernel Conditional Density Operators AISTATS 2020
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms AISTATS 2020
Learning Gaussian Graphical Models via Multiplicative Weights AISTATS 2020
On Generalization Bounds of a Family of Recurrent Neural Networks AISTATS 2020
Fast and Bayes-consistent nearest neighbors AISTATS 2020
A nonasymptotic law of iterated logarithm for general M-estimators AISTATS 2020
Two-sample Testing Using Deep Learning AISTATS 2020
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models AISTATS 2020
The Sylvester Graphical Lasso (SyGlasso) AISTATS 2020
Conditional Linear Regression AISTATS 2020
Approximate Cross-Validation in High Dimensions with Guarantees AISTATS 2020
Hypothesis Testing Interpretations and Renyi Differential Privacy AISTATS 2020
Learning Entangled Single-Sample Distributions via Iterative Trimming AISTATS 2020
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis AISTATS 2020
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference AISTATS 2020
Linear predictor on linearly-generated data with missing values: non consistency and solutions AISTATS 2020
Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation AISTATS 2020