variance estimation

Learning Entangled Single-Sample Gaussians in the Subset-of-Signals Model COLT 2020 On the Theoretical Properties of the Network Jackknife ICML 2020 Rethinking Bias-Variance Trade-off for Generalization of Neural Networks ICML 2020 Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers ICLR 2019 Reliable training and estimation of variance networks NeurIPS 2019 Empirical bounds for functions with weak interactions COLT 2018 Estimating Learnability in the Sublinear Data Regime NeurIPS 2018 Importance Sampling for Fair Policy Selection IJCAI 2018 Streaming kernel regression with provably adaptive mean, variance, and regularization JMLR 2018 Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples NeurIPS 2017 Minimax Regret Bounds for Reinforcement Learning ICML 2017 Thresholding Bandits with Augmented UCB IJCAI 2017 Learning the Variance of the Reward-To-Go JMLR 2016 Machine Learning in an Auction Environment JMLR 2016 Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models AISTATS 2016 Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife JMLR 2014 Estimating LASSO Risk and Noise Level NeurIPS 2013 Learning Heteroscedastic Models by Convex Programming under Group Sparsity ICML 2013 Multiple-source cross-validation ICML 2013 TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning NeurIPS 2011 Empirical Bernstein Inequalities for U-Statistics NeurIPS 2010 Gaussian sampling by local perturbations NeurIPS 2010 Data-driven calibration of linear estimators with minimal penalties NeurIPS 2009 PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier NeurIPS 2006 Analysis of Variance of Cross-Validation Estimators of the Generalization Error JMLR 2005