importance sampling

Simple, Distributed, and Accelerated Probabilistic Programming NeurIPS 2018 Stochastic Variance-Reduced Policy Gradient ICML 2018 Training Deep Models Faster with Robust, Approximate Importance Sampling NeurIPS 2018 Variance Reduction in Black-box Variational Inference by Adaptive Importance Sampling IJCAI 2018 Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization ICML 2018 Variational Inference with Tail-adaptive f-Divergence NeurIPS 2018 Weighted SGD for ℓ_p Regression with Randomized Preconditioning JMLR 2018 Yes, but Did It Work?: Evaluating Variational Inference ICML 2018 Black-box Importance Sampling AISTATS 2017 Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification AISTATS 2017 Dynamic Importance Sampling for Anytime Bounds of the Partition Function NeurIPS 2017 Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planning RSS 2017 Faster Coordinate Descent via Adaptive Importance Sampling AISTATS 2017 Identifying Best Interventions through Online Importance Sampling ICML 2017 Importance sampling for unbiased on-demand evaluation of knowledge base population EMNLP 2017 Lipschitz Density-Ratios, Structured Data, and Data-driven Tuning AISTATS 2017 Markov Chain Truncation for Doubly-Intractable Inference AISTATS 2017 Off-policy evaluation for slate recommendation NeurIPS 2017 On the Sampling Problem for Kernel Quadrature ICML 2017 Perturbative Black Box Variational Inference NeurIPS 2017 Post-Inference Prior Swapping ICML 2017 Safe Adaptive Importance Sampling NeurIPS 2017 Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning ICML 2017 VAE Learning via Stein Variational Gradient Descent NeurIPS 2017 Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning ICML 2016