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Jun-Ming Xu

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13 papers
2 author rows

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13

IJCAI Conference 2013 Conference Paper

Socioscope: Spatio-Temporal Signal Recovery from Social Media (Extended Abstract)

  • Jun-Ming Xu
  • Aniruddha Bhargava
  • Robert Nowak
  • Xiaojin Zhu

Counting the number of social media posts on a target phenomenon has become a popular method to monitor a spatiotemporal signal. However, such counting is plagued by biased, missing, or scarce data. We address these issues by formulating signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatiotemporal regularization into the model to address the data quality issues. Our model produces qualitatively convincing results in a case study on wildlife roadkill monitoring.

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TIST Journal 2012 Journal Article

Metric Learning for Estimating Psychological Similarities

  • Jun-Ming Xu
  • Xiaojin Zhu
  • Timothy T. Rogers

An important problem in cognitive psychology is to quantify the perceived similarities between stimuli. Previous work attempted to address this problem with multidimensional scaling (MDS) and its variants. However, there are several shortcomings of the MDS approaches. We propose Yada, a novel general metric-learning procedure based on two-alternative forced-choice behavioral experiments. Our method learns forward and backward nonlinear mappings between an objective space in which the stimuli are defined by the standard feature vector representation and a subjective space in which the distance between a pair of stimuli corresponds to their perceived similarity. We conduct experiments on both synthetic and real human behavioral datasets to assess the effectiveness of Yada. The results show that Yada outperforms several standard embedding and metric-learning algorithms, both in terms of likelihood and recovery error.

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AAAI Conference 2011 Conference Paper

OASIS: Online Active Semi-Supervised Learning

  • Andrew Goldberg
  • Xiaojin Zhu
  • Alex Furger
  • Jun-Ming Xu

We consider a learning setting of importance to large scale machine learning: potentially unlimited data arrives sequentially, but only a small fraction of it is labeled. The learner cannot store the data; it should learn from both labeled and unlabeled data, and it may also request labels for some of the unlabeled items. This setting is frequently encountered in real-world applications and has the characteristics of online, semi-supervised, and active learning. Yet previous learning models fail to consider these characteristics jointly. We present OASIS, a Bayesian model for this learning setting. The main contributions of the model include the novel integration of a semi-supervised likelihood function, a sequential Monte Carlo scheme for efficient online Bayesian updating, and a posterior-reduction criterion for active learning. Encouraging results on both synthetic and real-world optical character recognition data demonstrate the synergy of these characteristics in OASIS.

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