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Jiarong Jiang

I will be on the job market this fall.

Department of Computer Science
University of Maryland
A.V. Williams Building, Room 1103 (Office 3126)
College Park, MD 20742

Email: jiarong AT umiacs DOT umd DOT edu

I am currently a Ph.D. student at Department of Computer Science, University of Maryland, College Park. My advisor is Dr. Hal Daumé III. I am also associated with UMIACS (CLIP lab). My research interests are efficient approximate inference, graphical models, and structured prediction, particularly parsing.

I got my Bachelor's degree in Mathematics(Information and Computing Science) and second Bachelor's degree in Computer Science from Fudan University, Shanghai, China.


Jiarong Jiang, Taesun Moon, Hal Daumé III, Jason Einser, Prioritized Asynchronous Belief Propagation, ICML 2013 Workshop on Inferning. [abstract] | [slides]

Jiarong Jiang, Adam Teichert, Hal Daumé III, Jason Eisner, Learned Prioritization for Trading Off Accuracy and Speed, NIPS 12. [abstract] | [paper] | [poster]

Users want natural language processing (NLP) systems to be both fast and accurate, but quality often comes at the cost of speed. The field has been manually exploring various speed-accuracy tradeoffs for particular problems or datasets. We aim to explore this space automatically, focusing here on the case of agenda-based syntactic parsing (Kay, 1986). Unfortunately, off-the-shelf reinforcement learning techniques fail to learn good policies: the state space is too large to explore naively. We propose a hybrid reinforcement/apprenticeship learning algorithm that, even with few inexpensive features, can automatically learn weights that achieve competitive accuracies at significant improvements in speed over state-of-the-art baselines.

- A short version at ICML 2012 Workshop on Inferning: [paper] | [poster] | [slides]

Jiarong Jiang, Hal Daumé III, Q-learning on a multi-state MDP, Learning Workshop, 2012. (talk)

Jiarong Jiang, Piyush Rai, Hal Daumé III, Message-Passing for Approximate MAP Inference with Latent Variables, NIPS 2011. [abstract] | [paper]

We consider a general inference setting for discrete probabilistic graphical models where we seek maximum a posteriori (MAP) estimates for a subset of the random variables (max nodes), marginalizing over the rest (sum nodes). We present a hybrid message-passing algorithm to accomplish this. The hybrid algorithm passes a mix of sum and max messages depending on the type of source node (sum or max). We derive our algorithm by showing that it falls out as the solution of a particular relaxation of a variational framework. We further show that the Expectation Maximization algorithm can be seen as an approximation to our algorithm. Experimental results on synthetic and real-world datasets, against several baselines, demonstrate the efficacy of our proposed algorithm.

Jiarong Jiang, Adam Teichert, Hal Daumé III, Faster, Better, or Both! Learning Priority Functions for Decoding, Mid-Atlantic Student Colloquium on Speech, Language and Learning, 2011. (talk) [abstract] | [slides]

Amit Goyal, Jiarong Jiang, Hal Daumé III, Segmenting low-level instructions into high-level instructions, Learning Workshop, 2011. [abstract]

Jiarong Jiang, Piyush Rai, Hal Daumé III, Message Passing Algorithm for Marginal-MAP Estimation, Learning Workshop, 2010. [abstract]


Python Crash Course (Day 2), Language Science Winter Storm 2013. [slides | source code (.tar, .zip) | solution (.tar, .zip)]
- About how to use dictionary, file input, output.

Marginal-MAP source code [Coming soon]

A Java wrapper for Evalb [Here]

Last update: Sep 20, 2012
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