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Justin Domke
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2020 – today
- 2024
- [c42]Xi Wang, Tomas Geffner, Justin Domke:
Joint control variate for faster black-box variational inference. AISTATS 2024: 1639-1647 - [c41]Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke:
Simulation-Based Stacking. AISTATS 2024: 4267-4275 - [i36]Abhinav Agrawal, Justin Domke:
Understanding and mitigating difficulties in posterior predictive evaluation. CoRR abs/2405.19747 (2024) - [i35]Jinlin Lai, Daniel Sheldon, Justin Domke:
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models. CoRR abs/2410.24079 (2024) - 2023
- [j4]Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon:
U-Statistics for Importance-Weighted Variational Inference. Trans. Mach. Learn. Res. 2023 (2023) - [c40]Tomas Geffner, Justin Domke:
Langevin Diffusion Variational Inference. AISTATS 2023: 576-593 - [c39]Justin Domke, Robert M. Gower, Guillaume Garrigos:
Provable convergence guarantees for black-box variational inference. NeurIPS 2023 - [c38]Yuling Yao, Justin Domke:
Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier. NeurIPS 2023 - [i34]Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon:
U-Statistics for Importance-Weighted Variational Inference. CoRR abs/2302.13918 (2023) - [i33]Javier Burroni, Justin Domke, Daniel Sheldon:
Sample Average Approximation for Black-Box VI. CoRR abs/2304.06803 (2023) - [i32]Yuling Yao, Justin Domke:
Discriminative calibration. CoRR abs/2305.14593 (2023) - [i31]Justin Domke, Guillaume Garrigos, Robert M. Gower:
Provable convergence guarantees for black-box variational inference. CoRR abs/2306.03638 (2023) - [i30]Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke:
Simulation based stacking. CoRR abs/2310.17009 (2023) - 2022
- [j3]Ga Wu
, Justin Domke, Scott Sanner:
Arbitrary conditional inference in variational autoencoders via fast prior network training. Mach. Learn. 111(7): 2537-2559 (2022) - [c37]Jinlin Lai, Justin Domke, Daniel Sheldon:
Variational Marginal Particle Filters. AISTATS 2022: 875-895 - [c36]Tomas Geffner, Justin Domke:
Variational Inference with Locally Enhanced Bounds for Hierarchical Models. ICML 2022: 7310-7323 - [i29]Tomas Geffner, Justin Domke:
Variational Inference with Locally Enhanced Bounds for Hierarchical Models. CoRR abs/2203.04432 (2022) - [i28]Tomas Geffner, Justin Domke:
Langevin Diffusion Variational Inference. CoRR abs/2208.07743 (2022) - [i27]Xi Wang, Tomas Geffner, Justin Domke:
A Dual Control Variate for doubly stochastic optimization and black-box variational inference. CoRR abs/2210.07290 (2022) - 2021
- [c35]Tomas Geffner, Justin Domke:
On the difficulty of unbiased alpha divergence minimization. ICML 2021: 3650-3659 - [c34]Tomas Geffner, Justin Domke:
MCMC Variational Inference via Uncorrected Hamiltonian Annealing. NeurIPS 2021: 639-651 - [c33]Abhinav Agrawal, Justin Domke:
Amortized Variational Inference for Simple Hierarchical Models. NeurIPS 2021: 21388-21399 - [i26]Justin Domke:
An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulations. CoRR abs/2103.01030 (2021) - [i25]Tomas Geffner, Justin Domke:
Empirical Evaluation of Biased Methods for Alpha Divergence Minimization. CoRR abs/2105.06587 (2021) - [i24]Tomas Geffner, Justin Domke:
MCMC Variational Inference via Uncorrected Hamiltonian Annealing. CoRR abs/2107.04150 (2021) - [i23]Jinlin Lai, Daniel Sheldon, Justin Domke:
Variational Marginal Particle Filters. CoRR abs/2109.15134 (2021) - [i22]Abhinav Agrawal, Justin Domke:
Amortized Variational Inference for Simple Hierarchical Models. CoRR abs/2111.03144 (2021) - 2020
- [c32]Tomas Geffner, Justin Domke:
A Rule for Gradient Estimator Selection, with an Application to Variational Inference. AISTATS 2020: 1803-1812 - [c31]Justin Domke:
Provable Smoothness Guarantees for Black-Box Variational Inference. ICML 2020: 2587-2596 - [c30]Abhinav Agrawal, Daniel Sheldon, Justin Domke:
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. NeurIPS 2020 - [c29]Tomas Geffner, Justin Domke:
Approximation Based Variance Reduction for Reparameterization Gradients. NeurIPS 2020 - [i21]Justin Domke:
Moment-Matching Conditions for Exponential Families with Conditioning or Hidden Data. CoRR abs/2001.09771 (2020) - [i20]Abhinav Agrawal, Daniel Sheldon, Justin Domke:
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. CoRR abs/2006.10343 (2020) - [i19]Tomas Geffner, Justin Domke:
Approximation Based Variance Reduction for Reparameterization Gradients. CoRR abs/2007.14634 (2020) - [i18]Tomas Geffner, Justin Domke:
On the Difficulty of Unbiased Alpha Divergence Minimization. CoRR abs/2010.09541 (2020)
2010 – 2019
- 2019
- [c28]Justin Domke:
Provable Gradient Variance Guarantees for Black-Box Variational Inference. NeurIPS 2019: 328-337 - [c27]Justin Domke, Daniel Sheldon:
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation. NeurIPS 2019: 338-347 - [c26]My Phan, Yasin Abbasi-Yadkori, Justin Domke:
Thompson Sampling and Approximate Inference. NeurIPS 2019: 8801-8811 - [i17]Justin Domke:
Provable Smoothness Guarantees for Black-Box Variational Inference. CoRR abs/1901.08431 (2019) - [i16]Justin Domke:
Provable Gradient Variance Guarantees for Black-Box Variational Inference. CoRR abs/1906.08241 (2019) - [i15]Justin Domke, Daniel Sheldon:
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation. CoRR abs/1906.10115 (2019) - [i14]My Phan, Yasin Abbasi-Yadkori, Justin Domke:
Thompson Sampling and Approximate Inference. CoRR abs/1908.04970 (2019) - [i13]Tomas Geffner, Justin Domke:
A Rule for Gradient Estimator Selection, with an Application to Variational Inference. CoRR abs/1911.01894 (2019) - 2018
- [c25]Rui Li, Kishan KC, Feng Cui, Justin Domke, Anne R. Haake:
Sparse Covariance Modeling in High Dimensions with Gaussian Processes. NeurIPS 2018: 741-750 - [c24]Justin Domke, Daniel Sheldon:
Importance Weighting and Variational Inference. NeurIPS 2018: 4475-4484 - [c23]Tomas Geffner, Justin Domke:
Using Large Ensembles of Control Variates for Variational Inference. NeurIPS 2018: 9982-9992 - [i12]Ga Wu, Justin Domke, Scott Sanner:
Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding. CoRR abs/1805.07785 (2018) - [i11]Justin Domke, Daniel Sheldon:
Importance Weighting and Variational Inference. CoRR abs/1808.09034 (2018) - [i10]Tomas Geffner, Justin Domke:
Using Large Ensembles of Control Variates for Variational Inference. CoRR abs/1810.12482 (2018) - 2017
- [c22]Justin Domke:
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI. ICML 2017: 1029-1038 - [i9]Justin Domke:
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI. CoRR abs/1706.06529 (2017) - 2016
- [c21]Adrian Weller, Justin Domke:
Clamping Improves TRW and Mean Field Approximations. AISTATS 2016: 38-46 - 2015
- [c20]Ehsan Abbasnejad, Justin Domke, Scott Sanner:
Loss-Calibrated Monte Carlo Action Selection. AAAI 2015: 3447-3453 - [c19]Justin Domke:
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets. NIPS 2015: 874-882 - [c18]Hadi Mohasel Afshar, Justin Domke:
Reflection, Refraction, and Hamiltonian Monte Carlo. NIPS 2015: 3007-3015 - [i8]Justin Domke:
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets. CoRR abs/1509.08992 (2015) - [i7]Adrian Weller, Justin Domke:
Clamping Improves TRW and Mean Field Approximations. CoRR abs/1510.00087 (2015) - 2014
- [c17]Aaron Defazio, Justin Domke, Tibério S. Caetano:
Finito: A faster, permutable incremental gradient method for big data problems. ICML 2014: 1125-1133 - [c16]Xianghang Liu, Justin Domke:
Projecting Markov Random Field Parameters for Fast Mixing. NIPS 2014: 1377-1385 - [i6]Justin Domke, Xianghang Liu:
Projecting Ising Model Parameters for Fast Mixing. CoRR abs/1407.0749 (2014) - [i5]Justin Domke:
Structured Learning via Logistic Regression. CoRR abs/1407.0754 (2014) - [i4]Aaron J. Defazio, Tibério S. Caetano, Justin Domke:
Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems. CoRR abs/1407.2710 (2014) - [i3]Xianghang Liu, Justin Domke:
Projecting Markov Random Field Parameters for Fast Mixing. CoRR abs/1411.1119 (2014) - 2013
- [j2]Justin Domke:
Learning Graphical Model Parameters with Approximate Marginal Inference. IEEE Trans. Pattern Anal. Mach. Intell. 35(10): 2454-2467 (2013) - [c15]Justin Domke:
Structured Learning via Logistic Regression. NIPS 2013: 647-655 - [c14]Justin Domke, Xianghang Liu:
Projecting Ising Model Parameters for Fast Mixing. NIPS 2013: 665-673 - [i2]Justin Domke:
Learning Graphical Model Parameters with Approximate Marginal Inference. CoRR abs/1301.3193 (2013) - 2012
- [c13]Justin Domke:
Generic Methods for Optimization-Based Modeling. AISTATS 2012: 318-326 - [i1]Justin Domke:
Learning Convex Inference of Marginals. CoRR abs/1206.3247 (2012) - 2011
- [c12]Justin Domke:
Dual Decomposition for Marginal Inference. AAAI 2011: 1037-1042 - [c11]Justin Domke:
Parameter learning with truncated message-passing. CVPR 2011: 2937-2943 - 2010
- [c10]Justin Domke:
Implicit Differentiation by Perturbation. NIPS 2010: 523-531
2000 – 2009
- 2009
- [b1]Justin Domke:
Tractable Learning and Inference in High-Treewidth Graphical Models. University of Maryland, College Park, MD, USA, 2009 - [j1]Justin Domke, Yiannis Aloimonos:
Image Transformations and Blurring. IEEE Trans. Pattern Anal. Mach. Intell. 31(5): 811-823 (2009) - 2008
- [c9]Justin Domke, Alap Karapurkar, Yiannis Aloimonos:
Who killed the directed model? CVPR 2008 - [c8]Konstantinos Bitsakos, Justin Domke, Cornelia Fermüller, Yiannis Aloimonos:
Measuring 1st order stretchwith a single filter. ICASSP 2008: 909-912 - [c7]Justin Domke:
Learning Convex Inference of Marginals. UAI 2008: 137-144 - 2007
- [c6]Justin Domke, Yiannis Aloimonos:
Multiple View Image Reconstruction: A Harmonic Approach. CVPR 2007 - [c5]Justin Domke, Yiannis Aloimonos:
Signals on Pencils of Lines. ICCV 2007: 1-7 - 2006
- [c4]Justin Domke, Yiannis Aloimonos:
A Probabilistic Notion of Correspondence and the Epipolar Constraint. 3DPVT 2006: 41-48 - [c3]Justin Domke, Yiannis Aloimonos:
Deformation and Viewpoint Invariant Color Histograms. BMVC 2006: 509-518 - [c2]Justin Domke, Yiannis Aloimonos:
A Probabilistic Framework for Correspondence and Egomotion. WDV 2006: 232-242 - [c1]Justin Domke, Yiannis Aloimonos:
Integration of Visual and Inertial Information for Egomotion: a Stochastic Approach. ICRA 2006: 2053-2059
Coauthor Index
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