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Variational inference provides a powerful tool for approximate probabilistic in- ference on complex, structured models. Typical variational inference methods, however, require to use inference networks with computationally tractable proba-…

Machine Learning · Statistics 2017-11-01 Qiang Liu , Yihao Feng

Univariate or multivariate ordinal responses are often assumed to arise from a latent continuous parametric distribution, with covariate effects which enter linearly. We introduce a Bayesian nonparametric modeling approach for univariate…

Methodology · Statistics 2016-09-21 Maria DeYoreo , Athanasios Kottas

How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational…

Machine Learning · Statistics 2022-12-13 Diederik P Kingma , Max Welling

Variational inference is a fast and scalable alternative to Markov chain Monte Carlo and has been widely applied to posterior inference tasks in statistics and machine learning. A traditional approach for implementing mean-field variational…

Statistics Theory · Mathematics 2026-01-01 Qiang Du , Kaizheng Wang , Edith Zhang , Chenyang Zhong

We consider the problem of approximate Bayesian inference in log-supermodular models. These models encompass regular pairwise MRFs with binary variables, but allow to capture high-order interactions, which are intractable for existing…

Machine Learning · Computer Science 2015-02-25 Josip Djolonga , Andreas Krause

Current methods for learning graphical models with latent variables and a fixed structure estimate optimal values for the model parameters. Whereas this approach usually produces overfitting and suboptimal generalization performance,…

Machine Learning · Computer Science 2013-01-30 Hagai Attias

Differential analysis is a routine procedure in the statistical analysis toolbox across many applied fields, including quantitative proteomics, the main illustration of the present paper. The state-of-the-art limma approach uses a…

Methodology · Statistics 2025-12-12 Marie Chion , Arthur Leroy

Fitting cross-classified multilevel models with binary response is challenging. In this setting a promising method is Bayesian inference through Integrated Nested Laplace Approximations (INLA), which performs well in several latent variable…

Computation · Statistics 2016-07-21 Leonardo Grilli , Francesco Innocenti

Regression models with both high-dimensional responses and covariates have attracted growing attention. Standard multivariate regression models become inadequate when the response variables depend not only on observed covariates but also on…

Methodology · Statistics 2026-05-01 Jing Ouyang , Chengyu Cui , Yunxiao Chen , Kean Ming Tan , Gongjun Xu

Current variational inference methods for hierarchical Bayesian nonparametric models can neither characterize the correlation structure among latent variables due to the mean-field setting, nor infer the true posterior dimension because of…

Machine Learning · Statistics 2022-04-07 Yirui Liu , Xinghao Qiao , Jessica Lam

In this paper, we present a unified analysis of methods for such a wide class of problems as variational inequalities, which includes minimization problems and saddle point problems. We develop our analysis on the modified Extra-Gradient…

Optimization and Control · Mathematics 2023-04-18 Aleksandr Beznosikov , Alexander Gasnikov , Karina Zainulina , Alexander Maslovskiy , Dmitry Pasechnyuk

Modern methods for Bayesian regression beyond the Gaussian response setting are often computationally impractical or inaccurate in high dimensions. In fact, as discussed in recent literature, bypassing such a trade-off is still an open…

Methodology · Statistics 2022-04-14 Augusto Fasano , Daniele Durante , Giacomo Zanella

Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arising from space and time. In this paper, we propose location…

Machine Learning · Statistics 2017-07-04 Shiliang Sun , John Paisley , Qiuyang Liu

Deep Gaussian Processes (DGPs) are hierarchical generalizations of Gaussian Processes that combine well calibrated uncertainty estimates with the high flexibility of multilayer models. One of the biggest challenges with these models is that…

Machine Learning · Statistics 2018-11-13 Marton Havasi , José Miguel Hernández-Lobato , Juan José Murillo-Fuentes

Bayesian analyses combine information represented by different terms in a joint Bayesian model. When one or more of the terms is misspecified, it can be helpful to restrict the use of information from suspect model components to modify…

Methodology · Statistics 2022-06-27 Xuejun Yu , David J. Nott , Michael Stanley Smith

Variational logistic regression is a popular method for approximate Bayesian inference seeing wide-spread use in many areas of machine learning including: Bayesian optimization, reinforcement learning and multi-instance learning to name a…

Machine Learning · Statistics 2025-11-14 Michael Komodromos , Marina Evangelou , Sarah Filippi

Fast variational approximate algorithms are developed for Bayesian semiparametric regression when the response variable is a count, i.e. a non-negative integer. We treat both the Poisson and Negative Binomial families as models for the…

Methodology · Statistics 2013-09-18 Jan Luts , Matt P. Wand

We introduce priors and algorithms to perform Bayesian inference in Gaussian models defined by acyclic directed mixed graphs. Such a class of graphs, composed of directed and bi-directed edges, is a representation of conditional…

Methodology · Statistics 2012-07-02 Ricardo Silva , Zoubin Ghahramani

In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider…

Machine Learning · Statistics 2017-07-18 Thomas Brouwer , Jes Frellsen , Pietro Lió

Current statistics literature on statistical inference of random fields typically assumes that the fields are stationary or focuses on models of non-stationary Gaussian fields with parametric/semiparametric covariance families, which may…

Statistics Theory · Mathematics 2024-09-04 Yunyi Zhang , Zhou Zhou