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- Thomas Takeo Osugi, Kun Deng, Stephen D. Scott
- Fifth IEEE International Conference on Data…
- 2005

Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We… (More)

- Kun Deng, Prashant G. Mehta, Sean P. Meyn
- IEEE Trans. Automat. Contr.
- 2011

—This paper is concerned with model reduction for complex Markov chain models. The Kullback–Leibler divergence rate is employed as a metric to measure the difference between the Markov model and its approximation. For a certain relaxation of the bi-partition model reduction problem, the solution is shown to be characterized by an associated eigenvalue… (More)

- Matt Culver, Kun Deng, Stephen D. Scott
- Sixth International Conference on Data Mining…
- 2006

In active learning, a machine learning algorithm is given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. classification accuracy. We study how active… (More)

- Kun Deng, Chris Bourke, Stephen D. Scott, Julie Sunderman, Yaling Zheng
- Seventh IEEE International Conference on Data…
- 2007

We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples' labels but has to pay for each attribute that is specified. This learning model is appropriate in many areas, including medical applications. We present new algorithms for choosing which attributes to purchase of which examples in… (More)

- Kun Deng, Prabir Barooah, Prashant G. Mehta
- ACC
- 2012

- Chris Bourke, Kun Deng, Stephen D. Scott, Robert E. Schapire, N. V. Vinodchandran
- Machine Learning
- 2008

Significant changes in the instance distribution or associated cost function of a learning problem require one to reoptimize a previously-learned classifier to work under new conditions. We study the problem of reoptimizing a multi-class classifier based on its ROC hypersurface and a matrix describing the costs of each type of prediction error. For a binary… (More)

- Kun Deng, Joelle Pineau, Susan A. Murphy
- UAI
- 2011

The personalization of treatment via bio-markers and other risk categories has drawn increasing interest among clinical scientists. Personalized treatment strategies can be learned using data from clinical trials, but such trials are very costly to run. This paper explores the use of active learning techniques to design more efficient trials, addressing… (More)

- Shou-Jia Sun, Tao Su, Kun Deng, Bian Wu, Chang-Hong Liang
- IEEE Transactions on Microwave Theory and…
- 2013

In this paper, a short-ended stepped-impedance dual-resonance resonator is presented. Analysis of its resonance characteristics is carried out, from which the design graph is given. By controlling the impedance and length ratios of the resonator, for the first time, the first three pairs of resonant modes corresponding to three passbands within a single… (More)

- Kun Deng, Jian-Pei Zhang, Jing Yang
- 2015

- Bin Hu, Wei Wang, +17 authors Chengcai Chu
- Nature genetics
- 2015

Asian cultivated rice (Oryza sativa L.) consists of two main subspecies, indica and japonica. Indica has higher nitrate-absorption activity than japonica, but the molecular mechanisms underlying that activity remain elusive. Here we show that variation in a nitrate-transporter gene, NRT1.1B (OsNPF6.5), may contribute to this divergence in nitrate use.… (More)