← Authors

Justin Domke

33 papers · 2010–2025 · 4 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (15) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (4) πŸƒ Academic Marathon (15) 🐝 Cross-Pollinator (14) 🌈 Renaissance Researcher (5) 🐺 Lone Wolf (7) πŸ† Keyword Champion (3) πŸ”¬ Deep Specialist (20) πŸ’Ž Century Club (33) πŸ—ƒοΈ Keyword Collector (56) πŸ”₯ Unstoppable (14) πŸ“ˆ Trend Setter

Conferences

NIPS (18) AISTATS (8) ICML (6) UAI (1)

Top co-authors

Tomas Geffner (8) Abhinav Agrawal (4) Daniel R. Sheldon (3) Daniel Sheldon (3) Jinlin Lai (2) Xianghang Liu (2) Yuling Yao (2) Aaron Defazio (1) Robert Gower (1) Guillaume Garrigos (1)

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