Justin Domke
33 papers · 2010–2025 · 4 conferences · across top CS/AI conferences
Achievements
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(5)
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(7)
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(20)
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Century Club
(33)
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Conferences
NIPS (18)
AISTATS (8)
ICML (6)
UAI (1)
Top co-authors
Keywords
variational inference
(20)
markov chain monte carlo
(8)
reparameterization gradient
(4)
bayesian inference
(4)
control variate
(4)
stochastic optimization
(4)
graphical model
(4)
mixing time
(3)
approximate inference
(3)
importance weighting
(3)
posterior approximation
(3)
gradient variance
(3)
black-box inference
(3)
amortized inference
(2)
langevin dynamics
(2)
fast mixing
(2)
parameter projection
(2)
bayesian computation
(2)
markov random field
(2)
gibbs sampling
(2)
Papers
Understanding the difficulties of posterior predictive estimation
ICML 2025
Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI
AISTATS 2025
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
NIPS 2024
Sample Average Approximation for Black-Box Variational Inference
UAI 2024
Simulation-Based Stacking
AISTATS 2024
Joint control variate for faster black-box variational inference
AISTATS 2024
Provable convergence guarantees for black-box variational inference
NIPS 2023
Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier
NIPS 2023
Langevin Diffusion Variational Inference
AISTATS 2023
Variational Marginal Particle Filters
AISTATS 2022
Variational Inference with Locally Enhanced Bounds for Hierarchical Models
ICML 2022
MCMC Variational Inference via Uncorrected Hamiltonian Annealing
NIPS 2021
Amortized Variational Inference for Simple Hierarchical Models
NIPS 2021
On the difficulty of unbiased alpha divergence minimization
ICML 2021
Provable Smoothness Guarantees for Black-Box Variational Inference
ICML 2020
A Rule for Gradient Estimator Selection, with an Application to Variational Inference
AISTATS 2020
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
NIPS 2020
Approximation Based Variance Reduction for Reparameterization Gradients
NIPS 2020
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation
NIPS 2019
Thompson Sampling and Approximate Inference
NIPS 2019
Provable Gradient Variance Guarantees for Black-Box Variational Inference
NIPS 2019
Using Large Ensembles of Control Variates for Variational Inference
NIPS 2018
Importance Weighting and Variational Inference
NIPS 2018
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI
ICML 2017
Clamping Improves TRW and Mean Field Approximations
AISTATS 2016
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets
NIPS 2015
Reflection, Refraction, and Hamiltonian Monte Carlo
NIPS 2015
Finito: A faster, permutable incremental gradient method for big data problems
ICML 2014
Projecting Markov Random Field Parameters for Fast Mixing
NIPS 2014
Projecting Ising Model Parameters for Fast Mixing
NIPS 2013
Structured Learning via Logistic Regression
NIPS 2013
Generic Methods for Optimization-Based Modeling
AISTATS 2012
Implicit Differentiation by Perturbation
NIPS 2010