A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC

3 January 2025

Papers citing "A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC"

11 / 11 papers shown

Title
Binary classification based Monte Carlo simulation Elouan Argouarc'h F. Desbouvries 20 0 0 29 Jul 2023
Deep Metric Tensor Regularized Policy Gradient Gang Chen Victoria Huang 26 0 0 18 May 2023
A Survey of Quantum Alternatives to Randomized Algorithms: Monte Carlo Integration and Beyond P. Intallura Georgios Korpas Sudeepto Chakraborty Vyacheslav Kungurtsev Jakub Mareˇcek 14 11 0 08 Mar 2023
ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling Zheqi Zhu Yuchen Shi Pingyi Fan Chenghui Peng Khaled B. Letaief FedML 25 8 0 05 Oct 2022
CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies Mohamed Alami Chehboune F. Llorente Rim Kaddah Luca Martino Jesse Read 21 0 0 19 May 2022
Optimality in Noisy Importance Sampling F. Llorente Luca Martino Jesse Read D. Delgado 39 5 0 07 Jan 2022
Revisiting Bayesian Autoencoders with MCMC Rohitash Chandra Mahir Jain Manavendra Maharana P. Krivitsky UQCV BDL 29 17 0 13 Apr 2021
A Two Stage Adaptive Metropolis Algorithm Anirban Mondal Kai-Li Yin A. Mandal 22 1 0 01 Jan 2021
Rate-optimal refinement strategies for local approximation MCMC Andrew D. Davis Youssef Marzouk Aaron Smith Natesh Pillai 11 10 0 29 May 2020
Stability of Noisy Metropolis-Hastings F. Medina-Aguayo Anthony Lee Gareth O. Roberts 59 41 0 24 Mar 2015
Local Gaussian process approximation for large computer experiments R. Gramacy D. Apley 124 391 0 02 Mar 2013