I am currently a PhD Candidate at UBC where I am supervised by Kevin Leyton-Brown. I am broadly interested in ways we can use structral assumptions to generalize beyond typical IID settings. During my PhD, I have worked on methods for using deep networks for causal inference, and designing deep network architectures for exchangable arrays that provide strong generalization across a variety of domians.
I was fortunate to spend the summer of 2016 and 2017 at Microsoft Research where I worked with Greg Lewis and Matt Taddy on deep learning approaches for causal inference.
Curriculum Vitae. Contact:
Valid Causal Inference with (Some) Invalid Instruments. Jason Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown.
Exemplar guided active learning. Jason Hartford, Kevin Leyton-Brown, Hadas Raviv, Dan Padnos, Shahar Lev, Barak Lenz. NeurIPS 2020.
Identifying Valid Instruments via Effect Agreement. Jason Hartford, Kevin Leyton-Brown. “Do the right thing”: Machine Learning and Causal Inference for Improved Decision Making Workshop at NeurIPS, 2019.
Predicting Propositional Satisfiability via End-to-End Learning. Chris Cameron, Rex Chen, Jason Hartford, Kevin Leyton-Brown. AAAI 2020.
Predicting Propositional Satisfiability via End-to-End Learning. Chris Cameron, Rex Chen, Jason Hartford, Kevin Leyton-Brown. Graph Representation Learning Workshop at NeurIPS 2019 (superceded by AAAI 2020 paper).
Deep Models of Interactions Across Sets Jason Hartford, Devon R Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh. ICML 2018.
Deep IV: A Flexible Approach for Counterfactual Prediction Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy. ICML 2017. Code / Talk
Counterfactual Prediction with Deep Instrumental Variables Networks. Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy What if? Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems. NIPS Workshop, 2016
Deep Learning for Predicting Human Strategic Behavior. Jason Hartford, James R. Wright, Kevin Leyton-Brown. NIPS 2016 Oral Presentation. Code / Talk
2017 - CPSC 540 Graduate Machine Learning TA.
2017 - Intern Microsoft Research.
2016 - Intern Microsoft Research.
2014 - 2015 UBC Graduate TA for the CSPC 340 machine learning course and CSPC 301 Computing for Life Sciences course.
2013 - 2014 Associate Lecturer - University of Witwatersrand lecturing Microeconomics and Econometrics.
2016 - (Ongoing) Doctor of Philosophy - University of British Columbia
2014 - 2016 Master of Science in Computer Science - University of British Columbia - Thesis
2013 - 2014 Bachelor of Computer Science Honours - University of the Witwatersrand
2010 - 2011 Master of Economic Science - University of the Witwatersrand
2009 - 2010 Bachelor of Economic Science Honours - University of the Witwatersrand
2008 - 2009 Higher Diploma - Computer Science - University of the Witwatersrand
2005 - 2007 Bachelor of Science - Mathematical Statistics - University of the Witwatersrand
Here are some links to my other sites:
CPSC 340 Lab notes are here
The GT-DT website is here
Other social sites (which are updated with varying degrees of frequency) are listed below.