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Justin Domke

According to our database¹, Justin Domke authored at least 53 papers between 2006 and 2023.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Links

On csauthors.net:

Bibliography

2023

U-Statistics for Importance-Weighted Variational Inference.

[BibT_eX]

[DOI]

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Trans. Mach. Learn. Res., 2023

Simulation based stacking.

[BibT_eX]

[DOI]

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Bruno Régaldo-Saint Blancard

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CoRR, 2023

Discriminative calibration.

[BibT_eX]

[DOI]

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CoRR, 2023

Sample Average Approximation for Black-Box VI.

[BibT_eX]

[DOI]

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CoRR, 2023

Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Provable convergence guarantees for black-box variational inference.

[BibT_eX]

[DOI]

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Robert M. Gower

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Guillaume Garrigos

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Langevin Diffusion Variational Inference.

[BibT_eX]

[DOI]

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Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Arbitrary conditional inference in variational autoencoders via fast prior network training.

[BibT_eX]

[DOI]

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Mach. Learn., 2022

A Dual Control Variate for doubly stochastic optimization and black-box variational inference.

[BibT_eX]

[DOI]

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CoRR, 2022

Variational Inference with Locally Enhanced Bounds for Hierarchical Models.

[BibT_eX]

[DOI]

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Proceedings of the International Conference on Machine Learning, 2022

Variational Marginal Particle Filters.

[BibT_eX]

[DOI]

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Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Empirical Evaluation of Biased Methods for Alpha Divergence Minimization.

[BibT_eX]

[DOI]

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CoRR, 2021

An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulations.

[BibT_eX]

[DOI]

CoRR, 2021

MCMC Variational Inference via Uncorrected Hamiltonian Annealing.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Amortized Variational Inference for Simple Hierarchical Models.

[BibT_eX]

[DOI]

Abhinav Agrawal

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Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the difficulty of unbiased alpha divergence minimization.

[BibT_eX]

[DOI]

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Proceedings of the 38th International Conference on Machine Learning, 2021

2020

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

[BibT_eX]

[DOI]

CoRR, 2020

Approximation Based Variance Reduction for Reparameterization Gradients.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

[BibT_eX]

[DOI]

Abhinav Agrawal

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Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Provable Smoothness Guarantees for Black-Box Variational Inference.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

A Rule for Gradient Estimator Selection, with an Application to Variational Inference.

[BibT_eX]

[DOI]

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Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Thompson Sampling and Approximate Inference.

[BibT_eX]

[DOI]

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Yasin Abbasi-Yadkori

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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.

[BibT_eX]

[DOI]

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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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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CoRR, 2018

Using Large Ensembles of Control Variates for Variational Inference.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Importance Weighting and Variational Inference.

[BibT_eX]

[DOI]

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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.

[BibT_eX]

[DOI]

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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.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Clamping Improves TRW and Mean Field Approximations.

[BibT_eX]

[DOI]

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Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Hadi Mohasel Afshar

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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.

[BibT_eX]

[DOI]

Ehsan Abbasnejad

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Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Projecting Markov Random Field Parameters for Fast Mixing.

[BibT_eX]

[DOI]

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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.

[BibT_eX]

[DOI]

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Tibério S. Caetano

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Learning Graphical Model Parameters with Approximate Marginal Inference.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2013

Projecting Ising Model Parameters for Fast Mixing.

[BibT_eX]

[DOI]

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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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

2011

Parameter learning with truncated message-passing.

[BibT_eX]

[DOI]

Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

Dual Decomposition for Marginal Inference.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

2010

Implicit Differentiation by Perturbation.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

PhD thesis, 2009

Image Transformations and Blurring.

[BibT_eX]

[DOI]

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Yiannis Aloimonos

IEEE Trans. Pattern Anal. Mach. Intell., 2009

2008

Learning Convex Inference of Marginals.

[BibT_eX]

[DOI]

Proceedings of the UAI 2008, 2008

Measuring 1<sup>st</sup> order stretchwith a single filter.

[BibT_eX]

[DOI]

Konstantinos Bitsakos

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,

Cornelia Fermüller

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Yiannis Aloimonos

Proceedings of the IEEE International Conference on Acoustics, 2008

Who killed the directed model?

[BibT_eX]

[DOI]

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Alap Karapurkar

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Yiannis Aloimonos

Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008

2007

Signals on Pencils of Lines.

[BibT_eX]

[DOI]

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Yiannis Aloimonos

Proceedings of the IEEE 11th International Conference on Computer Vision, 2007

Multiple View Image Reconstruction: A Harmonic Approach.

[BibT_eX]

[DOI]

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Yiannis Aloimonos

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.

[BibT_eX]

[DOI]

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Yiannis Aloimonos

Proceedings of the 2006 IEEE International Conference on Robotics and Automation, 2006

A Probabilistic Framework for Correspondence and Egomotion.

[BibT_eX]

[DOI]

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Yiannis Aloimonos

Proceedings of the Dynamical Vision, ICCV 2005 and ECCV 2006 Workshops, 2006

Deformation and Viewpoint Invariant Color Histograms.

[BibT_eX]

[DOI]

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Yiannis Aloimonos

Proceedings of the British Machine Vision Conference 2006, 2006

A Probabilistic Notion of Correspondence and the Epipolar Constraint.

[BibT_eX]

[DOI]

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Yiannis Aloimonos

Proceedings of the 3rd International Symposium on 3D Data Processing, 2006

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