importance sampling

Automated Scalable Bayesian Inference via Hilbert Coresets JMLR 2019 Autoregressive Energy Machines ICML 2019 Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting NeurIPS 2019 Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems AAAI 2019 Combining parametric and nonparametric models for off-policy evaluation ICML 2019 Comparing Sample-Wise Learnability across Deep Neural Network Models AAAI 2019 CounterFactual Regression with Importance Sampling Weights IJCAI 2019 Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds ICLR 2019 Deep Value Model Predictive Control CoRL 2019 Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning ICML 2019 DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections NeurIPS 2019 Embarrassingly Parallel Inference for Gaussian Processes JMLR 2019 Embarrassingly Parallel MCMC using Deep Invertible Transformations UAI 2019 Energy-Inspired Models: Learning with Sampler-Induced Distributions NeurIPS 2019 Experimenting with Power Divergences for Language Modeling EMNLP 2019 Fast determinantal point processes via distortion-free intermediate sampling COLT 2019 Generalizing the theory of cooperative inference AISTATS 2019 Importance Resampling for Off-policy Prediction NeurIPS 2019 Importance Sampling Policy Evaluation with an Estimated Behavior Policy ICML 2019 Improving Quadrature for Constrained Integrands AISTATS 2019 Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks NeurIPS 2019 Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression COLT 2019 Nonconvex Variance Reduced Optimization with Arbitrary Sampling ICML 2019 Off-Policy Evaluation via Off-Policy Classification NeurIPS 2019 Off-Policy Policy Gradient with Stationary Distribution Correction UAI 2019