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06/04/2023
Provable convergence guarantees for black-box variational inference
While black-box variational inference is widely used, there is no proof ...
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05/24/2023
Discriminative calibration
To check the accuracy of Bayesian computations, it is common to use rank...
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04/13/2023
Sample Average Approximation for Black-Box VI
We present a novel approach for black-box VI that bypasses the difficult...
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02/27/2023
U-Statistics for Importance-Weighted Variational Inference
We propose the use of U-statistics to reduce variance for gradient estim...
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10/13/2022
A Dual Control Variate for doubly stochastic optimization and black-box variational inference
In this paper, we aim at reducing the variance of doubly stochastic opti...
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08/16/2022
Langevin Diffusion Variational Inference
Many methods that build powerful variational distributions based on unad...
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03/08/2022
Variational Inference with Locally Enhanced Bounds for Hierarchical Models
Hierarchical models represent a challenging setting for inference algori...
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11/04/2021
Amortized Variational Inference for Simple Hierarchical Models
It is difficult to use subsampling with variational inference in hierarc...
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09/30/2021
Variational Marginal Particle Filters
Variational inference for state space models (SSMs) is known to be hard ...
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07/08/2021
MCMC Variational Inference via Uncorrected Hamiltonian Annealing
Given an unnormalized target distribution we want to obtain approximate ...
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05/13/2021
Empirical Evaluation of Biased Methods for Alpha Divergence Minimization
In this paper we empirically evaluate biased methods for alpha-divergenc...
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02/25/2021
An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulations
It is important to estimate the errors of probabilistic inference algori...
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10/19/2020
On the Difficulty of Unbiased Alpha Divergence Minimization
Several approximate inference algorithms have been proposed to minimize ...
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07/29/2020
Approximation Based Variance Reduction for Reparameterization Gradients
Flexible variational distributions improve variational inference but are...
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06/18/2020
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
Recent research has seen several advances relevant to black-box VI, but ...
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11/05/2019
A Rule for Gradient Estimator Selection, with an Application to Variational Inference
Stochastic gradient descent (SGD) is the workhorse of modern machine lea...
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08/14/2019
Thompson Sampling and Approximate Inference
We study the effects of approximate inference on the performance of Thom...
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06/24/2019
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation
Recent work in variational inference (VI) uses ideas from Monte Carlo es...
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06/19/2019
Provable Gradient Variance Guarantees for Black-Box Variational Inference
Recent variational inference methods use stochastic gradient estimators ...
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01/24/2019
Provable Smoothness Guarantees for Black-Box Variational Inference
Black-box variational inference tries to approximate a complex target di...
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10/30/2018
Using Large Ensembles of Control Variates for Variational Inference
Variational inference is increasingly being addressed with stochastic op...
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08/27/2018
Importance Weighting and Variational Inference
Recent work used importance sampling ideas for better variational bounds...
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08/27/2018
Importance Weighting and Varational Inference
Recent work used importance sampling ideas for better variational bounds...
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05/20/2018
Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding
Variational Autoencoders (VAEs) are a popular generative model, but one ...
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06/20/2017
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI
Two popular classes of methods for approximate inference are Markov chai...
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10/01/2015
Clamping Improves TRW and Mean Field Approximations
We examine the effect of clamping variables for approximate inference in...
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09/30/2015
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets
Inference is typically intractable in high-treewidth undirected graphica...
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11/05/2014
Projecting Markov Random Field Parameters for Fast Mixing
Markov chain Monte Carlo (MCMC) algorithms are simple and extremely powe...
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07/10/2014
Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems
Recent advances in optimization theory have shown that smooth strongly c...
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07/03/2014
Structured Learning via Logistic Regression
A successful approach to structured learning is to write the learning ob...
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07/03/2014
Projecting Ising Model Parameters for Fast Mixing
Inference in general Ising models is difficult, due to high treewidth ma...
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01/15/2013