Advances in Variational Inference

Abstract

Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully used in various models and large-scale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects: (a) scalable VI, which includes stochastic approximations, (b) generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as non-conjugate models, (c) accurate VI, which includes variational models beyond the mean field approximation or with atypical divergences, and (d) amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.

References in corpus (38)

• ADADELTA: An Adaptive Learning Rate Method
• Loopy Belief Propagation for Approximate Inference: An Empirical Study
• Expectation Propagation for approximate Bayesian inference
• NICE: Non-linear Independent Components Estimation
• The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
• Determinantal point processes for machine learning
• Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
• Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
• Variational Lossy Autoencoder
• Edward: A library for probabilistic modeling, inference, and criticism
• Learning in Implicit Generative Models
• Autoencoding Variational Inference For Topic Models
• Towards Deeper Understanding of Variational Autoencoding Models
• Variational Bayesian Inference with Stochastic Search
• Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
• On integral probability metrics, ϕ-divergences and binary classification
• Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling
• Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
• Fast Variational Inference in the Conjugate Exponential Family
• Variational Inference using Implicit Distributions
• Variational Boosting: Iteratively Refining Posterior Approximations
• Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
• Structured Stochastic Variational Inference
• Boosting Variational Inference
• Stochastic Variational Inference for Hidden Markov Models
• Nonparametric variational inference
• Reducing Reparameterization Gradient Variance
• ZhuSuan: A Library for Bayesian Deep Learning
• On Nesting Monte Carlo Estimators
• Variational Message Passing with Structured Inference Networks
• Adversarial Message Passing For Graphical Models
• Design and Implementation of Probabilistic Programming Language Anglican
• Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions
• Faster Coordinate Descent via Adaptive Importance Sampling
• A trust-region method for stochastic variational inference with applications to streaming data
• Smoothed Gradients for Stochastic Variational Inference
• Structured Black Box Variational Inference for Latent Time Series Models
• Active Mini-Batch Sampling using Repulsive Point Processes

Cited by in corpus (16)

• Deep generative models in inversion: a review and development of a new approach based on a variational autoencoder
• Semi-Amortized Variational Autoencoders
• A Tutorial on Deep Latent Variable Models of Natural Language
• Stein Variational Gradient Descent Without Gradient
• Quasi-Monte Carlo Variational Inference
• Active Mini-Batch Sampling using Repulsive Point Processes
• Using Large Ensembles of Control Variates for Variational Inference
• Wasserstein Variational Inference
• The Bayesian Learning Rule
• Learning to Solve AC Optimal Power Flow by Differentiating through Holomorphic Embeddings
• On the Difficulty of Unbiased Alpha Divergence Minimization
• Scalable Approximate Inference and Some Applications
• Detect, anticipate and generate: Semi-supervised recurrent latent variable models for human activity modeling
• Multilevel Monte Carlo Variational Inference
• Approximation Based Variance Reduction for Reparameterization Gradients
• Structured Stochastic Gradient MCMC