Parameter Estimation

Automatic debiased machine learning and sensitivity analysis for sample selection models CLeaR 2026 Instance-optimal high-precision shadow tomography with few-copy measurements: A metrological approach COLT 2026 On Parameter Estimation in Deviated Gaussian Mixture of Experts AISTATS 2024 Contrastive Divergence Learning with Chained Belief Propagation PGM 2020 Gaussian Sum-Product Networks Learning in the Presence of Interval Censored Data PGM 2020 Noise-Contrastive Estimation for Multivariate Point Processes NeurIPS 2020 Learning Distributions Generated by One-Layer ReLU Networks NeurIPS 2019 Theoretical guarantees for EM under misspecified Gaussian mixture models NeurIPS 2018 Scalable Variational Inference for Dynamical Systems NeurIPS 2017 Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications ACML 2017 Reconstructing Parameters of Spreading Models from Partial Observations NeurIPS 2016 b-bit Marginal Regression NeurIPS 2015 Closed-form Estimators for High-dimensional Generalized Linear Models NeurIPS 2015 Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces NeurIPS 2015 Fast and Accurate Inference of Plackett–Luce Models NeurIPS 2015 High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality NeurIPS 2015 Learning with Relaxed Supervision NeurIPS 2015 Decomposing Parameter Estimation Problems NeurIPS 2014 Expectation-Maximization for Learning Determinantal Point Processes NeurIPS 2014 Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning NeurIPS 2012 Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs NeurIPS 2011 Shadow Dirichlet for Restricted Probability Modeling NeurIPS 2010 An improved estimator of Variance Explained in the presence of noise NeurIPS 2008