Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution, AAAI 2020 and NeurIPS 2019 Bayesian Deep Learning Workshop
-
Updated
Jan 12, 2021 - Jupyter Notebook
Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution, AAAI 2020 and NeurIPS 2019 Bayesian Deep Learning Workshop
This repository contains the source code of the paper Primary-Space Adaptive Control Variates using Piecewise-Polynomial Approximations by Miguel Crespo, Adrian Jarabo, and Adolfo Muñoz from ACM Transactions on Graphics.
VILTRUM: Varied Integration Layouts for arbiTRary integrals in a Unified Manner - A C++17 header-only library that provides a set of numerical integration routines
Unbiased Deep Learning based Solvers for parametric PDEs
Learning in Noisy MDP (which is governed by stochastic, exogenous input processes) with input-dependent baseline
Controlled importance-weighted cross-validation
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning (ICML 2022)
Vrednovanje azijskih opcija
Importance sampling with control variates on top of Distributions.jl
A pytorch-version implementation of RL algorithms. Now it collects TRPO, ClipPPO, A2C, GAIL and ADCV.
Project on using control variates for bayesian neural networks
Add a description, image, and links to the control-variates topic page so that developers can more easily learn about it.
To associate your repository with the control-variates topic, visit your repo's landing page and select "manage topics."