importance sampling

Tractable computation of expected kernels UAI 2021 Unbiased gradient estimation for variational auto-encoders using coupled Markov chains UAI 2021 Variational refinement for importance sampling using the forward Kullback-Leibler divergence UAI 2021 Weighted model estimation for offline model-based reinforcement learning NeurIPS 2021 A/B Testing in Dense Large-Scale Networks: Design and Inference NeurIPS 2020 A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions NeurIPS 2020 Active Adversarial Domain Adaptation WACV 2020 Adaptive Estimator Selection for Off-Policy Evaluation ICML 2020 Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes NeurIPS 2020 A Generative Model for Molecular Distance Geometry ICML 2020 Amortized Population Gibbs Samplers with Neural Sufficient Statistics ICML 2020 A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control AAAI 2020 A Stochastic Derivative Free Optimization Method with Momentum ICLR 2020 Balanced Off-Policy Evaluation in General Action Spaces AISTATS 2020 Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning ICLR 2020 Bridging the Gap Between f-GANs and Wasserstein GANs ICML 2020 Conditional Importance Sampling for Off-Policy Learning AISTATS 2020 Decision-Making with Auto-Encoding Variational Bayes NeurIPS 2020 Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support ICML 2020 Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation ICLR 2020 Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies NeurIPS 2020 f-Divergence Variational Inference NeurIPS 2020 From Importance Sampling to Doubly Robust Policy Gradient ICML 2020 IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks ICLR 2020 Importance Sampling Techniques for Policy Optimization JMLR 2020