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This paper introduces Distributed Stein Variational Gradient Descent (DSVGD), a non-parametric generalized Bayesian inference framework for federated learning.
Variational federated multi-task learning
Luca Corinzia and Joachim M Buhmann · 1906
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Variational federated multi-task learning
Luca Corinzia and Joachim M Buhmann · 1906
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Information and information stability of random variables and processes
Mark S Pinsker · 1964
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The Comparison and Evaluation of Forecasters
Morris H. DeGroot and Stephen E. Fienberg · 1983
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Natural gradient works efficiently in learning
Shun-Ichi Amari · 1998
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Fisher discriminant analysis with kernels
S. Mika, G. Ratsch, J. Weston, B. Scholkopf, and K. R. Mullers · 1999
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Online Model Selection Based on the Variational Bayes
M. Sato · 2001
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Information Theory, Inference & Learning Algorithms
David J. C. MacKay · 2002
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Predicting Good Probabilities with Supervised Learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop · 2006
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Information-theoretic upper and lower bounds for statistical estimation
Tong Zhang · 2006
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Derivative reproducing properties for kernel methods in learning theory
Ding-Xuan Zhou · 2008
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Bayesian Learning via Stochastic Gradient Langevin Dynamics
Max Welling and Yee Whye Teh · 2011
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Nonparametric Variational Inference
Samuel J. Gershman, Matthew D. Hoffman, and David M. Blei · 2012
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Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
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Streaming Variational Bayes
Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C Wilson, and Michael I Jordan · 2013
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Parallel MCMC via Weierstrass Sampler
Xiangyu Wang and David B. Dunson · 2013
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Distributed Stochastic Gradient MCMC
Sungjin Ahn, Babak Shahbaba, and Max Welling · 2014
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Distributed Estimation, Information Loss and Exponential Families
Qiang Liu and Alexander Ihler · 2014
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Probabilistic backpropagation for scalable learning of bayesian neural networks
José Miguel Hernández-Lobato and Ryan P. Adams · 2015
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Distilling the Knowledge in a Neural Network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Embarrassingly parallel variational inference in nonconjugate models
Willie Neiswanger, Chong Wang, and Eric Xing · 2015
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On the properties of variational approximations of Gibbs posteriors
Pierre Alquier, James Ridgway, and Nicolas Chopin · 2016
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Generalized variational inference
Jeremias Knoblauch, Jack Jewson, and Theodoros Damoulas · 2019
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On the Validity of Bayesian Neural Networks for Uncertainty Estimation
John Mitros and Brian Mac Namee · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Parallel Markov chain Monte Carlo for Bayesian hierarchical models with big data, in two stages
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Elaine Angelino, Matthew James Johnson, and Ryan P Adams · 2016
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Provable Bayesian Inference via Particle Mirror Descent
Bo Dai, Niao He, Hanjun Dai, and Le Song · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
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A kernelized Stein discrepancy for goodness-of-fit tests
Qiang Liu, Jason Lee, and Michael Jordan · 2016
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Bayes and Big Data: The Consensus Monte Carlo Algorithm
Steven L. Scott, Alexander W. Blocker, Fernando V. Bonassi, Hugh A. Chipman, Edward I. George, and Robert E. McCulloch · 2016
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On Calibration of Modern Neural Networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Zheng Wei and Erin M Conlon · 2019
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Bayesian Nonparametric Federated Learning of Neural Networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni · 2019
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Model Fusion with Kullback–Leibler Divergence
Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, and Justin Solomon · 2020
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Hands-on Bayesian Neural Networks–a Tutorial for Deep Learning Users
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A Non-Asymptotic Analysis for Stein Variational Gradient Descent
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Federated Learning with Communication Delay in Edge Networks
Frank Po-Chen Lin, Christopher G Brinton, and Nicolò Michelusi · 2020
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Embarrassingly Parallel MCMC using Deep Invertible Transformations
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Fast-Convergent Federated Learning
Hung T Nguyen, Vikash Sehwag, Seyyedali Hosseinalipour, Christopher G Brinton, Mung Chiang, and H Vincent Poor · 2020
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FedSplit: An algorithmic framework for fast federated optimization
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Stein Variational Gaussian Processes
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Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data
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Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
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Fed{be}: Making bayesian model ensemble applicable to federated learning
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