Domke, Justin

45 publications

AISTATS 2025 Disentangling Impact of Capacity, Objective, Batchsize, Estimators, and Step-Size on Flow VI Abhinav Agrawal, Justin Domke

NeurIPS 2025 Large Language Bayes Justin Domke

NeurIPS 2025 Model-Informed Flows for Bayesian Inference Joohwan Ko, Justin Domke

ICML 2025 Understanding the Difficulties of Posterior Predictive Estimation Abhinav Agrawal, Justin Domke

NeurIPS 2024 Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models Jinlin Lai, Justin Domke, Daniel Sheldon

AISTATS 2024 Joint Control Variate for Faster Black-Box Variational Inference Xi Wang, Tomas Geffner, Justin Domke

UAI 2024 Sample Average Approximation for Black-Box Variational Inference Javier Burroni, Justin Domke, Daniel Sheldon

AISTATS 2024 Simulation-Based Stacking Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke

NeurIPS 2023 Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier Yuling Yao, Justin Domke

AISTATS 2023 Langevin Diffusion Variational Inference Tomas Geffner, Justin Domke

NeurIPS 2023 Provable Convergence Guarantees for Black-Box Variational Inference Justin Domke, Robert Gower, Guillaume Garrigos

TMLR 2023 U-Statistics for Importance-Weighted Variational Inference Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon

AISTATS 2022 Variational Marginal Particle Filters Jinlin Lai, Justin Domke, Daniel Sheldon

MLJ 2022 Arbitrary Conditional Inference in Variational Autoencoders via Fast Prior Network Training Ga Wu, Justin Domke, Scott Sanner

ICML 2022 Variational Inference with Locally Enhanced Bounds for Hierarchical Models Tomas Geffner, Justin Domke

NeurIPS 2021 Amortized Variational Inference for Simple Hierarchical Models Abhinav Agrawal, Justin Domke

NeurIPS 2021 MCMC Variational Inference via Uncorrected Hamiltonian Annealing Tomas Geffner, Justin Domke

ICML 2021 On the Difficulty of Unbiased Alpha Divergence Minimization Tomas Geffner, Justin Domke

AISTATS 2020 A Rule for Gradient Estimator Selection, with an Application to Variational Inference Tomas Geffner, Justin Domke

NeurIPS 2020 Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization Abhinav Agrawal, Daniel R. Sheldon, Justin Domke

NeurIPS 2020 Approximation Based Variance Reduction for Reparameterization Gradients Tomas Geffner, Justin Domke

ICML 2020 Provable Smoothness Guarantees for Black-Box Variational Inference Justin Domke

NeurIPS 2019 Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation Justin Domke, Daniel R. Sheldon

NeurIPS 2019 Provable Gradient Variance Guarantees for Black-Box Variational Inference Justin Domke

NeurIPS 2019 Thompson Sampling and Approximate Inference My Phan, Yasin Abbasi Yadkori, Justin Domke

NeurIPS 2018 Importance Weighting and Variational Inference Justin Domke, Daniel R. Sheldon

NeurIPS 2018 Using Large Ensembles of Control Variates for Variational Inference Tomas Geffner, Justin Domke

ICML 2017 A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI Justin Domke

AISTATS 2016 Clamping Improves TRW and Mean Field Approximations Adrian Weller, Justin Domke

AAAI 2015 Loss-Calibrated Monte Carlo Action Selection Ehsan Abbasnejad, Justin Domke, Scott Sanner

NeurIPS 2015 Maximum Likelihood Learning with Arbitrary Treewidth via Fast-Mixing Parameter Sets Justin Domke

NeurIPS 2015 Reflection, Refraction, and Hamiltonian Monte Carlo Hadi Mohasel Afshar, Justin Domke

ICML 2014 Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems Aaron Defazio, Justin Domke, Caetano

NeurIPS 2014 Projecting Markov Random Field Parameters for Fast Mixing Xianghang Liu, Justin Domke

NeurIPS 2013 Projecting Ising Model Parameters for Fast Mixing Justin Domke, Xianghang Liu

NeurIPS 2013 Structured Learning via Logistic Regression Justin Domke

AISTATS 2012 Generic Methods for Optimization-Based Modeling Justin Domke

AAAI 2011 Dual Decomposition for Marginal Inference Justin Domke

CVPR 2011 Parameter Learning with Truncated Message-Passing Justin Domke

NeurIPS 2010 Implicit Differentiation by Perturbation Justin Domke

UAI 2008 Learning Convex Inference of Marginals Justin Domke

CVPR 2008 Who Killed the Directed Model? Justin Domke, Alap Karapurkar, Yiannis Aloimonos

CVPR 2007 Multiple View Image Reconstruction: A Harmonic Approach Justin Domke, Yiannis Aloimonos

ICCV 2007 Signals on Pencils of Lines Justin Domke, Yiannis Aloimonos

ECCV 2006 A Probabilistic Framework for Correspondence and Egomotion Justin Domke, Yiannis Aloimonos