Justin Domke

According to our database1, Justin Domke authored at least 36 papers between 2006 and 2020.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

On csauthors.net:

Bibliography

2020
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization.
CoRR, 2020

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

A Rule for Gradient Estimator Selection, with an Application to Variational Inference.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Provable Smoothness Guarantees for Black-Box Variational Inference.
CoRR, 2019

Thompson Sampling and Approximate Inference.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Provable Gradient Variance Guarantees for Black-Box Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding.
CoRR, 2018

Using Large Ensembles of Control Variates for Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Importance Weighting and Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Sparse Covariance Modeling in High Dimensions with Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Clamping Improves TRW and Mean Field Approximations.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Reflection, Refraction, and Hamiltonian Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Loss-Calibrated Monte Carlo Action Selection.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Projecting Markov Random Field Parameters for Fast Mixing.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Finito: A faster, permutable incremental gradient method for big data problems.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Learning Graphical Model Parameters with Approximate Marginal Inference.
IEEE Trans. Pattern Anal. Mach. Intell., 2013

Projecting Ising Model Parameters for Fast Mixing.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Structured Learning via Logistic Regression.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012
Generic Methods for Optimization-Based Modeling.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

2011
Parameter learning with truncated message-passing.
Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

Dual Decomposition for Marginal Inference.
Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

2010
Implicit Differentiation by Perturbation.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009
Tractable Learning and Inference in High-Treewidth Graphical Models.
PhD thesis, 2009

Image Transformations and Blurring.
IEEE Trans. Pattern Anal. Mach. Intell., 2009

2008
Learning Convex Inference of Marginals.
Proceedings of the UAI 2008, 2008

Measuring 1st order stretchwith a single filter.
Proceedings of the IEEE International Conference on Acoustics, 2008

Who killed the directed model?
Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008

2007
Signals on Pencils of Lines.
Proceedings of the IEEE 11th International Conference on Computer Vision, 2007

Multiple View Image Reconstruction: A Harmonic Approach.
Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 2007

2006
Integration of Visual and Inertial Information for Egomotion: a Stochastic Approach.
Proceedings of the 2006 IEEE International Conference on Robotics and Automation, 2006

A Probabilistic Framework for Correspondence and Egomotion.
Proceedings of the Dynamical Vision, ICCV 2005 and ECCV 2006 Workshops, 2006

Deformation and Viewpoint Invariant Color Histograms.
Proceedings of the British Machine Vision Conference 2006, 2006

A Probabilistic Notion of Correspondence and the Epipolar Constraint.
Proceedings of the 3rd International Symposium on 3D Data Processing, 2006


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