Jonathan Huggins aka jonathan huggins, jonathan h huggins, jhuggins

         PhD Candidate in Computer Science

Computer Science and Artifical Intelligence Laboratory (CSAIL)
Massachusetts Institute of Technology

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My current research interests are in developing better algorithms for probabilistic and statistical inference and improving our theoretical understanding of existing inference algorithms. My focus is on Bayesian inference, learning theory, and the interplay between efficient learning and inference. I am particularly interested in: the learning-theoretic properties of inference algorithms for hierarchical models, probabilistic programs, and other rich model classes; algorithms for large-scale Bayesian inference; and inference algorithms for rich model classes.

recent news

We have updated our preprint Truncated Random Measures, which has been completely rewritten to include more general truncation bounds and applications to a wider range of sequential representations.

Quantifying the Accuracy of Approximate Diffusions and Markov Chains, coauthored with James Zou, has been accepted to the Conference on Artificial Intelligence and Statistics.

James Zou and I have updated our preprint Quantifying the Accuracy of Approximate Diffusions and Markov Chains, which now includes an application of our approach to piecewise deterministic Markov processes, including zig-zag processes.

Coresets for Scalable Bayesian Logistic Regression, coauthored with Trevor Campbell and Tamara Broderick, has been accepted to the Conference on Neural Information Processing Systems.

Academic Bio

I am a fifth-year PhD student at MIT EECS/CSAIL, where I'm co-advised by Josh Tenenbaum and Tamara Broderick, but also spend time with Ryan Adam's HIPS group at Harvard. Before coming to MIT I was a mathematics major at Columbia University, where I worked with Frank Wood on Bayesian nonparametric modeling and with Liam Paninski on statistical methods for neuroscience.


Tamara Broderick, Trevor Campbell, Matthew J. Johnson, Vikash K. Mansinghka, Karthik Narasimhan Ari Pakman, Liam Paninski, Eftychios A. Pnevmatikakis, Kamiar Rahnama Rad, Dan Roy, Cynthia Rudin, Ardavan Saeedi, Carl Smith, Josh B. Tenenbaum, Frank Wood, James Zou

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preprints and working papers

Truncated Random Measures
Trevor Campbell*, Jonathan H. Huggins*, Jonathan P. How, Tamara Broderick
*Contributed equally

Convergence of Sequential Monte Carlo-based Sampling Methods
(with Daniel M. Roy)

recent publicationsand research articles view all

Quantifying the Accuracy of Approximate Diffusions and Markov Chains
(with James Zou)
In Proc. of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.

Coresets for Scalable Bayesian Logistic Regression
Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
In Proc. of Advances in Neural Information Processing Systems (NIPS), 2016.
[spotlight video] [AABI NIPS workshop talk video] [code]

Risk and Regret of Hierarchical Bayesian Learners
Jonathan H. Huggins, Joshua B. Tenenbaum
In Proc. of the 32nd International Conference on Machine Learning (ICML), 2015.

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
Jonathan H. Huggins*, Karthik Narasimhan*, Ardavan Saeedi*, Vikash L. Mansinghka
*Contributed equally
In Proc. of the 32nd International Conference on Machine Learning (ICML), 2015.

A statistical learning theory framework for supervised pattern discovery
Jonathan H. Huggins, Cynthia Rudin
In Proc. of SIAM International Conference on Data Mining (SDM), 2014.

Unpublished Work

Detailed Derivations of Small-variance Asymptotics for some Hierarchical Bayesian Nonparametric Models
Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson
arXiv:1501.00052 [stat.ML], 2014

Infinite Structured Hidden Semi-Markov Models
Jonathan H. Huggins, Frank Wood
arXiv:1407.0044 [stat.ME], 2014

Provably Learning Mixtures of Gaussians and More
Jonathan H. Huggins
A review paper for Rocco Servedio's computational learning theory class, 2011. Now very out-of-date.

In Fall 2011, for a final project in Stephen Edwards' compilers class, David Hu, Hans Hyttinen, Harley McGrew and I created YAPPL (Yet Another Probabilistic Programming Language). The final report includes a short tutorial and the language reference manual. The code for the compiler is written in OCaml, which is one of my favorite programming languages.


contact info

no recruiters, please.

Email:  jhuggins -at- mit edu
Curriculum Vitæ: PDF
Bitbucket: jhhuggins
Google Scholar: profile

Stata Center, Room G480
32 Vassar Street
Cambridge, MA 02139