Justin Domke

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 51

Top Venues

CoRR

NeurIPS

AISTATS

ICML

University of Maryland, College Park, MD, USA (PHD)

Education History

University of Maryland, College Park, MD, USA

PHD

Publications

Xi Wang, Tomas Geffner, Justin Domke
Joint control variate for faster black-box variational inference. AISTATS (2024)
Abhinav Agrawal, Justin Domke
Understanding and mitigating difficulties in posterior predictive evaluation. CoRR (2024)
Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke
Simulation-Based Stacking. AISTATS (2024)
Justin Domke, Robert M. Gower, Guillaume Garrigos
Provable convergence guarantees for black-box variational inference. NeurIPS (2023)
Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon
U-Statistics for Importance-Weighted Variational Inference. CoRR (2023)
Justin Domke, Guillaume Garrigos, Robert M. Gower
Provable convergence guarantees for black-box variational inference. CoRR (2023)
Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke
Simulation based stacking. CoRR (2023)
Tomas Geffner, Justin Domke
Langevin Diffusion Variational Inference. AISTATS (2023)
Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon
U-Statistics for Importance-Weighted Variational Inference. Trans. Mach. Learn. Res. 2023 (2023)
Yuling Yao, Justin Domke
Discriminative calibration. CoRR (2023)
Javier Burroni, Justin Domke, Daniel Sheldon
Sample Average Approximation for Black-Box VI. CoRR (2023)
Yuling Yao, Justin Domke
Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier. NeurIPS (2023)
Xi Wang, Tomas Geffner, Justin Domke
A Dual Control Variate for doubly stochastic optimization and black-box variational inference. CoRR (2022)
Tomas Geffner, Justin Domke
Variational Inference with Locally Enhanced Bounds for Hierarchical Models. ICML (2022)
Tomas Geffner, Justin Domke
Variational Inference with Locally Enhanced Bounds for Hierarchical Models. CoRR (2022)
Ga Wu, Justin Domke, Scott Sanner
Arbitrary conditional inference in variational autoencoders via fast prior network training. Mach. Learn. 111 (7) (2022)
Jinlin Lai, Justin Domke, Daniel Sheldon
Variational Marginal Particle Filters. AISTATS (2022)
Tomas Geffner, Justin Domke
Langevin Diffusion Variational Inference. CoRR (2022)
Abhinav Agrawal, Justin Domke
Amortized Variational Inference for Simple Hierarchical Models. NeurIPS (2021)
Abhinav Agrawal, Justin Domke
Amortized Variational Inference for Simple Hierarchical Models. CoRR (2021)
Tomas Geffner, Justin Domke
Empirical Evaluation of Biased Methods for Alpha Divergence Minimization. CoRR (2021)
Jinlin Lai, Daniel Sheldon, Justin Domke
Variational Marginal Particle Filters. CoRR (2021)
Justin Domke
An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulations. CoRR (2021)
Tomas Geffner, Justin Domke
MCMC Variational Inference via Uncorrected Hamiltonian Annealing. NeurIPS (2021)
Tomas Geffner, Justin Domke
On the difficulty of unbiased alpha divergence minimization. ICML (2021)
Tomas Geffner, Justin Domke
MCMC Variational Inference via Uncorrected Hamiltonian Annealing. CoRR (2021)
Tomas Geffner, Justin Domke
A Rule for Gradient Estimator Selection, with an Application to Variational Inference. AISTATS (2020)
Tomas Geffner, Justin Domke
On the Difficulty of Unbiased Alpha Divergence Minimization. CoRR (2020)
Abhinav Agrawal, Daniel Sheldon, Justin Domke
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. CoRR (2020)
Justin Domke
Provable Smoothness Guarantees for Black-Box Variational Inference. ICML (2020)
Tomas Geffner, Justin Domke
Approximation Based Variance Reduction for Reparameterization Gradients. CoRR (2020)
Tomas Geffner, Justin Domke
Approximation Based Variance Reduction for Reparameterization Gradients. NeurIPS (2020)
Justin Domke
Moment-Matching Conditions for Exponential Families with Conditioning or Hidden Data. CoRR (2020)
Abhinav Agrawal, Daniel R. Sheldon, Justin Domke
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. NeurIPS (2020)
Justin Domke
Provable Gradient Variance Guarantees for Black-Box Variational Inference. CoRR (2019)
Justin Domke, Daniel R. Sheldon
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation. NeurIPS (2019)
Justin Domke, Daniel Sheldon
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation. CoRR (2019)
Justin Domke
Provable Gradient Variance Guarantees for Black-Box Variational Inference. NeurIPS (2019)
My Phan, Yasin Abbasi-Yadkori, Justin Domke
Thompson Sampling and Approximate Inference. CoRR (2019)
Tomas Geffner, Justin Domke
A Rule for Gradient Estimator Selection, with an Application to Variational Inference. CoRR (2019)
Justin Domke
Provable Smoothness Guarantees for Black-Box Variational Inference. CoRR (2019)
My Phan, Yasin Abbasi-Yadkori, Justin Domke
Thompson Sampling and Approximate Inference. NeurIPS (2019)
Ga Wu, Justin Domke, Scott Sanner
Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding. CoRR (2018)
Tomas Geffner, Justin Domke
Using Large Ensembles of Control Variates for Variational Inference. NeurIPS (2018)
Tomas Geffner, Justin Domke
Using Large Ensembles of Control Variates for Variational Inference. CoRR (2018)
Justin Domke, Daniel Sheldon
Importance Weighting and Variational Inference. CoRR (2018)
Justin Domke, Daniel R. Sheldon
Importance Weighting and Variational Inference. NeurIPS (2018)
Rui Li, Kishan KC, Feng Cui, Justin Domke, Anne R. Haake
Sparse Covariance Modeling in High Dimensions with Gaussian Processes. NeurIPS (2018)
Justin Domke
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI. ICML (2017)
Justin Domke
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI. CoRR (2017)
Adrian Weller, Justin Domke
Clamping Improves TRW and Mean Field Approximations. AISTATS (2016)
Adrian Weller, Justin Domke
Clamping Improves TRW and Mean Field Approximations. CoRR (2015)
Ehsan Abbasnejad, Justin Domke, Scott Sanner
Loss-Calibrated Monte Carlo Action Selection. AAAI (2015)
Hadi Mohasel Afshar, Justin Domke
Reflection, Refraction, and Hamiltonian Monte Carlo. NIPS (2015)
Justin Domke
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets. NIPS (2015)
Justin Domke
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets. CoRR (2015)
Justin Domke, Xianghang Liu
Projecting Ising Model Parameters for Fast Mixing. CoRR (2014)
Aaron Defazio, Justin Domke, Tibério S. Caetano
Finito: A faster, permutable incremental gradient method for big data problems. ICML (2014)
Xianghang Liu, Justin Domke
Projecting Markov Random Field Parameters for Fast Mixing. NIPS (2014)
Justin Domke
Structured Learning via Logistic Regression. CoRR (2014)
Xianghang Liu, Justin Domke
Projecting Markov Random Field Parameters for Fast Mixing. CoRR (2014)
Aaron J. Defazio, Tibério S. Caetano, Justin Domke
Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems. CoRR (2014)
Justin Domke
Structured Learning via Logistic Regression. NIPS (2013)
Justin Domke, Xianghang Liu
Projecting Ising Model Parameters for Fast Mixing. NIPS (2013)
Justin Domke
Learning Graphical Model Parameters with Approximate Marginal Inference. IEEE Trans. Pattern Anal. Mach. Intell. 35 (10) (2013)
Justin Domke
Learning Graphical Model Parameters with Approximate Marginal Inference CoRR (2013)
Justin Domke
Generic Methods for Optimization-Based Modeling. AISTATS (2012)
Justin Domke
Learning Convex Inference of Marginals CoRR (2012)
Justin Domke
Dual Decomposition for Marginal Inference. AAAI (2011)
Justin Domke
Parameter learning with truncated message-passing. CVPR (2011)
Justin Domke
Implicit Differentiation by Perturbation. NIPS (2010)
Justin Domke, Yiannis Aloimonos
Image Transformations and Blurring. IEEE Trans. Pattern Anal. Mach. Intell. 31 (5) (2009)
Konstantinos Bitsakos, Justin Domke, Cornelia Fermüller, Yiannis Aloimonos
order stretchwith a single filter. ICASSP (2008)
Justin Domke
Learning Convex Inference of Marginals. UAI (2008)
Justin Domke, Alap Karapurkar, Yiannis Aloimonos
Who killed the directed model? CVPR (2008)
Justin Domke, Yiannis Aloimonos
Signals on Pencils of Lines. ICCV (2007)
Justin Domke, Yiannis Aloimonos
Multiple View Image Reconstruction: A Harmonic Approach. CVPR (2007)
Justin Domke, Yiannis Aloimonos
Integration of Visual and Inertial Information for Egomotion: a Stochastic Approach. ICRA (2006)
Justin Domke, Yiannis Aloimonos
A Probabilistic Notion of Correspondence and the Epipolar Constraint. 3DPVT (2006)
Justin Domke, Yiannis Aloimonos
A Probabilistic Framework for Correspondence and Egomotion. WDV (2006)
Justin Domke, Yiannis Aloimonos
Deformation and Viewpoint Invariant Color Histograms. BMVC (2006)

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Publication Activity (10 Years)

Top Topics

Top Venues

Justin Domke