Justin Domke
Assistant Professor
College of Computing and Information Sciences
University of Massachusetts, Amherst
email: (my last name)@cs.umass.edu
phone: +1 585 545 3241
office: CICS 208

Research Interests
Machine Learning, Probabilistic Graphical Models, Convex Optimization, Structured Learning

Recent Publications

Using Large Ensembles of Control Variates for Variational Inference, NeurIPS 2018 (with T. Geffner)

Importance Weighting and Variational Inference, NeurIPS 2018 (with D. Sheldon) [poster]

A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI, ICML 2017 [slides] [poster] [blog]

Distributed Solar Prediction with Wind Velocity, IEEE Photovoltaic Specialists Conference 2016 (with N. Engerer, A. Menon and C Webers)

Clamping Improves TRW and Mean Field Approximations, AISTATS 2016 (with A. Weller)

Reflection, Refraction, and Hamiltonian Monte Carlo, NIPS 2015 (with H. Afshar)

Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets, NIPS 2015

Loss-calibrated Monte Carlo Action Selection, AAAI 2015 (with E. Abbasnejad and S. Sanner)

Projecting General Markov Random Fields for Fast Mixing, NIPS 2014 (with X. Liu)

Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems, ICML 2014 (with A. Defazio and T. Caetano) [talk]

Weblog
justindomke.wordpress.com

Software
Graphical Models Toolbox (Matlab toolbox)
MARBL (Command line C++ tool, also usable on clusters.)

Prospective Student information

Teaching
(at UMass)
Spring 2019 Machine Learning, COMPSCI 589
Spring 2019 Graphical Models, COMPSCI 688
Fall 2017 Machine Learning, COMPSCI 589
Spring 2017 Graphical Models, COMPSCI 688
Fall 2016 Machine Learning, COMPSCI 589
(at ANU)
Semester 2 2014 Advanced Topics in Statistical Machine Learning (Structured Probabilistic Models)
Semester 2 2013 Advanced Topics in Statistical Machine Learning (Convex Optimization)
(at RIT)
Spring 2011-2012 Statistical Machine Learning
Winter 2011-2012 Discovery
Spring 2010-2011 Statistical Machine Learning
Winter 2010-2011 Discovery
Winter 2010-2011 Artificial Intelligence
Spring 2009-2010 Statistical Machine Learning