importance sampling

Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation NeurIPS 2023 Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes NeurIPS 2023 DMIS: Dynamic Mesh-Based Importance Sampling for Training Physics-Informed Neural Networks AAAI 2023 Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence JMLR 2023 Energy-Based Sliced Wasserstein Distance NeurIPS 2023 ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret ICLR 2023 Fast Offline Policy Optimization for Large Scale Recommendation AAAI 2023 Flow Annealed Importance Sampling Bootstrap ICLR 2023 GFlowNets and variational inference ICLR 2023 Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models ICLR 2023 HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent AISTATS 2023 Imitation Learning from Imperfection: Theoretical Justifications and Algorithms NeurIPS 2023 Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning NeurIPS 2023 In-sample Actor Critic for Offline Reinforcement Learning ICLR 2023 KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training NeurIPS 2023 Leveraging Importance Weights in Subset Selection ICLR 2023 Marginalized Importance Sampling for Off-Environment Policy Evaluation CoRL 2023 Massively parallel reweighted wake-sleep UAI 2023 Maximum Likelihood Estimation of Flexible Survival Densities with Importance Sampling MLHC 2023 Mitigating Dataset Bias by Using Per-Sample Gradient ICLR 2023 Modified Retrace for Off-Policy Temporal Difference Learning UAI 2023 Multiple-policy High-confidence Policy Evaluation AISTATS 2023 On the Relation between Policy Improvement and Off-Policy Minimum-Variance Policy Evaluation UAI 2023 On the Reuse Bias in Off-Policy Reinforcement Learning IJCAI 2023 On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples AAAI 2023