Wasserstein Gradient Boosting: A General Framework with Applications to Posterior Regression

15 May 2024

Papers citing "Wasserstein Gradient Boosting: A General Framework with Applications to Posterior Regression"

20 / 20 papers shown

Title
Benchmarking state-of-the-art gradient boosting algorithms for classification Piotr Florek Adam Zagdañski 48 13 0 26 May 2023
Regularized Stein Variational Gradient Flow Ye He Krishnakumar Balasubramanian Bharath K. Sriperumbudur Jianfeng Lu OT 51 12 0 15 Nov 2022
A stochastic Stein Variational Newton method Alex Leviyev Joshua Chen Yifei Wang Omar Ghattas A. Zimmerman 50 9 0 19 Apr 2022
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 229 1,150 0 07 Jul 2021
Projected Wasserstein gradient descent for high-dimensional Bayesian inference Yifei Wang Peng Chen Wuchen Li 46 26 0 12 Feb 2021
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges Moloud Abdar Farhad Pourpanah Sadiq Hussain Dana Rezazadegan Li Liu ... Xiaochun Cao Abbas Khosravi U. Acharya V. Makarenkov S. Nahavandi BDL UQCV 341 1,922 0 12 Nov 2020
DEAL: Deep Evidential Active Learning for Image Classification Patrick Hemmer Niklas Kühl Jakob Schöffer 68 38 0 22 Jul 2020
A Non-Asymptotic Analysis for Stein Variational Gradient Descent Anna Korba Adil Salim Michael Arbel Giulia Luise Arthur Gretton 61 78 0 17 Jun 2020
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts Bertrand Charpentier Daniel Zügner Stephan Günnemann UQCV UD EDL BDL 70 184 0 16 Jun 2020
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence Sinho Chewi Thibaut Le Gouic Chen Lu Tyler Maunu Philippe Rigollet 90 70 0 03 Jun 2020
Information Newton's flow: second-order optimization method in probability space Yifei Wang Wuchen Li 84 31 0 13 Jan 2020
NGBoost: Natural Gradient Boosting for Probabilistic Prediction Tony Duan Anand Avati D. Ding Khanh K. Thai S. Basu A. Ng Alejandro Schuler BDL 34 302 0 08 Oct 2019
Deep Evidential Regression Alexander Amini Wilko Schwarting A. Soleimany Daniela Rus EDL PER BDL UD UQCV 79 440 0 07 Oct 2019
A Stein variational Newton method Gianluca Detommaso Tiangang Cui Alessio Spantini Youssef Marzouk Robert Scheichl 113 117 0 08 Jun 2018
Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy Lance M. Kaplan M. Kandemir OOD UQCV EDL BDL 182 996 0 05 Jun 2018
Concrete Dropout Y. Gal Jiri Hron Alex Kendall BDL UQCV 179 592 0 22 May 2017
Stein Variational Gradient Descent as Gradient Flow Qiang Liu OT 77 276 0 25 Apr 2017
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 70 1,092 0 16 Aug 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 821 9,318 0 06 Jun 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 130 945 0 18 Feb 2015