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Sampling methods (e.g., node-wise, layer-wise, or subgraph) has become an indispensable strategy to speed up training large-scale Graph Neural Networks (GNNs). However, existing sampling methods are mostly based on the graph structural…

Machine Learning · Computer Science 2021-09-07 Weilin Cong , Rana Forsati , Mahmut Kandemir , Mehrdad Mahdavi

While Bayesian methods are extremely popular in statistics and machine learning, their application to massive datasets is often challenging, when possible at all. Indeed, the classical MCMC algorithms are prohibitively slow when both the…

Statistics Theory · Mathematics 2019-04-23 Pierre Alquier , James Ridgway

Although linear regression models are fundamental tools in statistical science, the estimation results can be sensitive to outliers. While several robust methods have been proposed in frequentist frameworks, statistical inference is not…

Methodology · Statistics 2020-07-15 Shintaro Hashimoto , Shonosuke Sugasawa

Because of their multimodality, mixture posterior distributions are difficult to sample with standard Markov chain Monte Carlo (MCMC) methods. We propose a strategy to enhance the sampling of MCMC in this context, using a biasing procedure…

Computation · Statistics 2011-04-19 Nicolas Chopin , Tony Lelievre , Gabriel Stoltz

The fairness of a deep neural network is strongly affected by dataset bias and spurious correlations, both of which are usually present in modern feature-rich and complex visual datasets. Due to the difficulty and variability of the task,…

Computer Vision and Pattern Recognition · Computer Science 2024-02-28 Rebecca S Stone , Nishant Ravikumar , Andrew J Bulpitt , David C Hogg

Bayesian inverse problems often involve sampling posterior distributions on infinite-dimensional function spaces. Traditional Markov chain Monte Carlo (MCMC) algorithms are characterized by deteriorating mixing times upon mesh-refinement,…

Computation · Statistics 2017-03-08 Alexandros Beskos , Mark Girolami , Shiwei Lan , Patrick E. Farrell , Andrew M. Stuart

In performing a Bayesian analysis, two difficult problems often emerge. First, in estimating the parameters of some model for the data, the resulting posterior distribution may be multi-modal or exhibit pronounced (curving) degeneracies.…

Instrumentation and Methods for Astrophysics · Physics 2013-12-20 F. Feroz , J. Skilling

This work introduces a Bayesian methodology for fitting large discrete graphical models with spike-and-slab priors to encode sparsity. We consider a quasi-likelihood approach that enables node-wise parallel computation resulting in reduced…

Methodology · Statistics 2019-10-21 Anwesha Bhattacharyya , Yves Atchade

Bayesian inference for inverse problems hinges critically on the choice of priors. In the absence of specific prior information, population-level distributions can serve as effective priors for parameters of interest. With the advent of…

Instrumentation and Methods for Astrophysics · Physics 2025-02-11 Gabriel Missael Barco , Alexandre Adam , Connor Stone , Yashar Hezaveh , Laurence Perreault-Levasseur

Bayesian Neural Networks (BNNs) offer a principled and natural framework for proper uncertainty quantification in the context of deep learning. They address the typical challenges associated with conventional deep learning methods, such as…

Computation · Statistics 2024-11-13 Zahra Moslemi , Yang Meng , Shiwei Lan , Babak Shahbaba

We propose a new approach to mixed-frequency regressions in a high-dimensional environment that resorts to Group Lasso penalization and Bayesian techniques for estimation and inference. In particular, to improve the prediction properties of…

Econometrics · Economics 2020-06-12 Matteo Mogliani , Anna Simoni

The true posterior distribution of a Bayesian neural network is massively multimodal. Whilst most of these modes are functionally equivalent, we demonstrate that there remains a level of real multimodality that manifests in even the…

Machine Learning · Statistics 2022-05-24 David Yallup , Will Handley , Mike Hobson , Anthony Lasenby , Pablo Lemos

Inverse problems constrained by partial differential equations are often ill-conditioned due to noisy and incomplete data or inherent non-uniqueness. A prominent example is full waveform inversion, which estimates Earth's subsurface…

Geophysics · Physics 2026-03-03 Ali Siahkoohi , Kamal Aghazade , Ali Gholami

Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same…

Computer Vision and Pattern Recognition · Computer Science 2022-11-08 Shichao Kan , Zhiquan He , Yigang Cen , Yang Li , Vladimir Mladenovic , Zhihai He

Bayesian low-rank matrix factorization techniques have become an essential tool for relational data analysis and matrix completion. A standard approach is to assign zero-mean Gaussian priors on the columns or rows of factor matrices to…

Machine Learning · Statistics 2020-11-11 Saibal De , Hadi Salehi , Alex Gorodetsky

Bayesian neural networks (BNNs) allow us to reason about uncertainty in a principled way. Stochastic Gradient Langevin Dynamics (SGLD) enables efficient BNN learning by drawing samples from the BNN posterior using mini-batches. However,…

Machine Learning · Computer Science 2018-06-28 Kuan-Chieh Wang , Paul Vicol , James Lucas , Li Gu , Roger Grosse , Richard Zemel

Exact-sparsity inducing prior distributions in Bayesian analysis typically lead to posterior distributions that are very challenging to handle by standard Markov Chain Monte Carlo (MCMC) methods, particular in high-dimensional models with…

Statistics Theory · Mathematics 2016-06-28 Yves F. Atchadé

Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems. In general, this has a cubic cost in dataset size and is sensitive to…

Bayesian Neural Networks (BNNs) provide principled estimates of model and data uncertainty by encoding parameters as distributions. This makes them key enablers for reliable AI that can be deployed on safety critical edge systems. These…

Emerging Technologies · Computer Science 2024-11-13 Prabodh Katti , Bashir M. Al-Hashimi , Bipin Rajendran

Some machine learning applications require continual learning - where data comes in a sequence of datasets, each is used for training and then permanently discarded. From a Bayesian perspective, continual learning seems straightforward:…

Machine Learning · Statistics 2019-02-19 Sebastian Farquhar , Yarin Gal
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