Meta-Learning for Stochastic Gradient MCMC

12 June 2018

Wenbo Gong

Yingzhen Li

José Miguel Hernández-Lobato

BDL

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Papers citing "Meta-Learning for Stochastic Gradient MCMC"

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Title
Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo Hyunsu Kim G. Nam Chulhee Yun Hongseok Yang Juho Lee BDL UQCV 57 0 0 02 Mar 2025
Bayesian Computation in Deep Learning Wenlong Chen Bolian Li Ruqi Zhang Yingzhen Li BDL 75 0 0 25 Feb 2025
Learning to Explore for Stochastic Gradient MCMC Seunghyun Kim Seohyeon Jung Seonghyeon Kim Juho Lee BDL 48 1 0 17 Aug 2024
S $^2$ AC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic Safa Messaoud Billel Mokeddem Zhenghai Xue L. Pang Bo An Haipeng Chen Sanjay Chawla 41 3 0 02 May 2024
Neural Structure Learning with Stochastic Differential Equations Benjie Wang Joel Jennings Wenbo Gong CML AI4TS 15 3 0 06 Nov 2023
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery Yashas Annadani Nick Pawlowski Joel Jennings Stefan Bauer Cheng Zhang Wenbo Gong CML BDL 15 17 0 26 Jul 2023
Machine Learning and the Future of Bayesian Computation Steven Winter Trevor Campbell Lizhen Lin Sanvesh Srivastava David B. Dunson TPM 33 4 0 21 Apr 2023
VeLO: Training Versatile Learned Optimizers by Scaling Up Luke Metz James Harrison C. Freeman Amil Merchant Lucas Beyer ... Naman Agrawal Ben Poole Igor Mordatch Adam Roberts Jascha Narain Sohl-Dickstein 26 60 0 17 Nov 2022
Approximate blocked Gibbs sampling for Bayesian neural networks Theodore Papamarkou BDL 138 2 0 24 Aug 2022
Practical tradeoffs between memory, compute, and performance in learned optimizers Luke Metz C. Freeman James Harrison Niru Maheswaranathan Jascha Narain Sohl-Dickstein 33 32 0 22 Mar 2022
Is MC Dropout Bayesian? Loic Le Folgoc V. Baltatzis S. Desai A. Devaraj S. Ellis O. M. Manzanera A. Nair Huaqi Qiu J. Schnabel Ben Glocker BDL OOD UQCV 22 39 0 08 Oct 2021
LSB: Local Self-Balancing MCMC in Discrete Spaces Emanuele Sansone 7 10 0 08 Sep 2021
Structured Stochastic Gradient MCMC Antonios Alexos Alex Boyd Stephan Mandt BDL 11 12 0 19 Jul 2021
Confidence-Aware Learning for Camouflaged Object Detection Jiawei Liu Jing Zhang Nick Barnes 26 13 0 22 Jun 2021
Meta-Learning an Inference Algorithm for Probabilistic Programs Gwonsoo Che Hongseok Yang TPM 11 1 0 01 Mar 2021
The Benefit of the Doubt: Uncertainty Aware Sensing for Edge Computing Platforms Lorena Qendro Jagmohan Chauhan Alberto Gil C. P. Ramos Cecilia Mascolo 12 6 0 11 Feb 2021
Bayesian neural networks and dimensionality reduction Deborshee Sen Theodore Papamarkou David B. Dunson BDL 12 4 0 18 Aug 2020
Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation Yichen Shen Zhilu Zhang M. Sabuncu Lin Sun UQCV 18 3 0 31 Jul 2020
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs Meng Qu Tianyu Gao Louis-Pascal Xhonneux Jian Tang BDL 16 106 0 05 Jul 2020
Being Bayesian about Categorical Probability Taejong Joo U. Chung Minji Seo UQCV BDL 22 58 0 19 Feb 2020
Inferring the Optimal Policy using Markov Chain Monte Carlo Brandon Trabucco A. Qu Simon Li Ganeshkumar Ashokavardhanan OffRL 11 0 0 16 Nov 2019
Challenges in Markov chain Monte Carlo for Bayesian neural networks Theodore Papamarkou Jacob D. Hinkle M. T. Young D. Womble BDL 31 50 0 15 Oct 2019
MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning Zhijun Mai Guosheng Hu Dexiong Chen Fumin Shen Heng Tao Shen 13 41 0 27 Aug 2019
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout Zhilu Zhang Adrian V. Dalca M. Sabuncu UQCV BDL 6 47 0 23 Jun 2019
Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation Erik Englesson Hossein Azizpour UQCV 14 7 0 12 Jun 2019
Exponential Family Estimation via Adversarial Dynamics Embedding Bo Dai Z. Liu H. Dai Niao He A. Gretton Le Song Dale Schuurmans 9 52 0 27 Apr 2019
Deterministic Variational Inference for Robust Bayesian Neural Networks Anqi Wu Sebastian Nowozin Edward Meeds Richard E. Turner José Miguel Hernández-Lobato Alexander L. Gaunt UQCV AAML BDL 29 16 0 09 Oct 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 317 11,681 0 09 Mar 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015
MCMC using Hamiltonian dynamics Radford M. Neal 185 3,262 0 09 Jun 2012