A Probabilistic Notion of Correspondence and the Epipolar Constraint. - Justin Domke; Yiannis Aloimonos (3DPVT, 2006)

Deformation and Viewpoint Invariant Color Histograms. - Justin Domke; Yiannis Aloimonos (BMVC, 2006)

A Probabilistic Framework for Correspondence and Egomotion. - Justin Domke; Yiannis Aloimonos (WDV, 2006)

Integration of Visual and Inertial Information for Egomotion: a Stochastic Approach. - Justin Domke; Yiannis Aloimonos (ICRA, 2006)

Multiple View Image Reconstruction: A Harmonic Approach. - Justin Domke; Yiannis Aloimonos (CVPR, 2007)

Signals on Pencils of Lines. - Justin Domke; Yiannis Aloimonos (ICCV, 2007)

Who killed the directed model? - Justin Domke; Alap Karapurkar; Yiannis Aloimonos (CVPR, 2008)

Measuring 1st order stretchwith a single filter. - Konstantinos Bitsakos; Justin Domke; Cornelia Fermüller; Yiannis Aloimonos (ICASSP, 2008)

Learning Convex Inference of Marginals. - Justin Domke (UAI, 2008)

Image Transformations and Blurring. - Justin Domke; Yiannis Aloimonos (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

Tractable Learning and Inference in High-Treewidth Graphical Models. - Justin Domke (2009)

Implicit Differentiation by Perturbation. - Justin Domke (NIPS, 2010)

Dual Decomposition for Marginal Inference. - Justin Domke (AAAI, 2011)

Parameter learning with truncated message-passing. - Justin Domke (CVPR, 2011)

Generic Methods for Optimization-Based Modeling. - Justin Domke (AISTATS, 2012)

Structured Learning via Logistic Regression. - Justin Domke (NIPS, 2013)

Projecting Ising Model Parameters for Fast Mixing. - Justin Domke; Xianghang Liu (NIPS, 2013)

Learning Graphical Model Parameters with Approximate Marginal Inference. - Justin Domke (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Finito: A faster, permutable incremental gradient method for big data problems. - Aaron Defazio; Justin Domke; Tibério S. Caetano (ICML, 2014)

Projecting Markov Random Field Parameters for Fast Mixing. - Xianghang Liu; Justin Domke (NIPS, 2014)

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

Reflection, Refraction, and Hamiltonian Monte Carlo. - Hadi Mohasel Afshar; Justin Domke (NIPS, 2015)

Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets. - Justin Domke (NIPS, 2015)

Clamping Improves TRW and Mean Field Approximations. - Adrian Weller; Justin Domke (AISTATS, 2016)

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

Sparse Covariance Modeling in High Dimensions with Gaussian Processes. - Rui Li 002; Kishan KC; Feng Cui; Justin Domke; Anne R. Haake (NeurIPS, 2018)

Importance Weighting and Variational Inference. - Justin Domke; Daniel Sheldon (NeurIPS, 2018)

Using Large Ensembles of Control Variates for Variational Inference. - Tomas Geffner; Justin Domke (NeurIPS, 2018)

Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding. - Ga Wu; Justin Domke; Scott Sanner (CoRR, 2018)

Provable Gradient Variance Guarantees for Black-Box Variational Inference. - Justin Domke (NeurIPS, 2019)

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

Thompson Sampling and Approximate Inference. - My Phan; Yasin Abbasi-Yadkori; Justin Domke (NeurIPS, 2019)

A Rule for Gradient Estimator Selection, with an Application to Variational Inference. - Tomas Geffner; Justin Domke (AISTATS, 2020)

Provable Smoothness Guarantees for Black-Box Variational Inference. - Justin Domke (ICML, 2020)

Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. - Abhinav Agrawal 001; Daniel Sheldon; Justin Domke (NeurIPS, 2020)

Approximation Based Variance Reduction for Reparameterization Gradients. - Tomas Geffner; Justin Domke (NeurIPS, 2020)

Moment-Matching Conditions for Exponential Families with Conditioning or Hidden Data. - Justin Domke (CoRR, 2020)

On the difficulty of unbiased alpha divergence minimization. - Tomas Geffner; Justin Domke (ICML, 2021)

Amortized Variational Inference for Simple Hierarchical Models. - Abhinav Agrawal 001; Justin Domke (NeurIPS, 2021)

MCMC Variational Inference via Uncorrected Hamiltonian Annealing. - Tomas Geffner; Justin Domke (NeurIPS, 2021)

An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulations. - Justin Domke (CoRR, 2021)

Empirical Evaluation of Biased Methods for Alpha Divergence Minimization. - Tomas Geffner; Justin Domke (CoRR, 2021)

Variational Marginal Particle Filters. - Jinlin Lai; Justin Domke; Daniel Sheldon (AISTATS, 2022)

Variational Inference with Locally Enhanced Bounds for Hierarchical Models. - Tomas Geffner; Justin Domke (ICML, 2022)

A Dual Control Variate for doubly stochastic optimization and black-box variational inference. - Xi Wang; Tomas Geffner; Justin Domke (CoRR, 2022)

Arbitrary conditional inference in variational autoencoders via fast prior network training. - Ga Wu; Justin Domke; Scott Sanner (Mach. Learn., 2022)

Langevin Diffusion Variational Inference. - Tomas Geffner; Justin Domke (AISTATS, 2023)

Provable convergence guarantees for black-box variational inference. - Justin Domke; Robert M. Gower; Guillaume Garrigos (NeurIPS, 2023)

Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier. - Yuling Yao; Justin Domke (NeurIPS, 2023)

Sample Average Approximation for Black-Box VI. - Javier Burroni; Justin Domke; Daniel Sheldon (CoRR, 2023)

Discriminative calibration. - Yuling Yao; Justin Domke (CoRR, 2023)

U-Statistics for Importance-Weighted Variational Inference. - Javier Burroni; Kenta Takatsu; Justin Domke; Daniel Sheldon (Trans. Mach. Learn. Res., 2023)

Joint control variate for faster black-box variational inference. - Xi Wang; Tomas Geffner; Justin Domke (AISTATS, 2024)

Simulation-Based Stacking. - Yuling Yao; Bruno Régaldo-Saint Blancard; Justin Domke (AISTATS, 2024)

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

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

Understanding and mitigating difficulties in posterior predictive evaluation. - Abhinav Agrawal 001; Justin Domke (CoRR, 2024)

Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI. - Abhinav Agrawal 001; Justin Domke (AISTATS, 2025)

Large Language Bayes. - Justin Domke (CoRR, 2025)

Model Informed Flows for Bayesian Inference of Probabilistic Programs. - Joohwan Ko; Justin Domke (CoRR, 2025)