monte carlo method

On Adaptive Distance Estimation NeurIPS 2020 Scaling Up AND/OR Abstraction Sampling IJCAI 2020 Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling ICML 2020 Adaptive Optimization Framework for Control of Multi-Agent Systems AAAI 2019 Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates NeurIPS 2019 A Statistical Approach to Assessing Neural Network Robustness ICLR 2019 Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives ICLR 2019 GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks IJCAI 2019 Inverse Path Tracing for Joint Material and Lighting Estimation CVPR 2019 Not All FPRASs are Equal: Demystifying FPRASs for DNF-Counting (Extended Abstract) IJCAI 2019 Scalable Collapsed Inference for High-Dimensional Topic Models NAACL 2019 Tensor Monte Carlo: Particle Methods for the GPU era NeurIPS 2019 Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods CoRL 2019 Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines AAAI 2019 A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference JMLR 2018 Asymptotic optimality of adaptive importance sampling NeurIPS 2018 FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling ICLR 2018 Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses NAACL 2018 Quadrature-based features for kernel approximation NeurIPS 2018 Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation NeurIPS 2018 Black-box Importance Sampling 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 On Perturbed Proximal Gradient Algorithms JMLR 2017 Variational Particle Approximations JMLR 2017