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Ruichen Qian

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2 papers

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NeurIPS Conference 2008 Conference Paper

Human Active Learning

Rui Castro
Charles Kalish
Robert Nowak
Ruichen Qian
Tim Rogers
Jerry Zhu

We investigate a topic at the interface of machine learning and cognitive science. Human active learning, where learners can actively query the world for information, is contrasted with passive learning from random examples. Furthermore, we compare human active learning performance with predictions from statistical learning theory. We conduct a series of human category learning experiments inspired by a machine learning task for which active and passive learning error bounds are well understood, and dramatically distinct. Our results indicate that humans are capable of actively selecting informative queries, and in doing so learn better and faster than if they are given random training data, as predicted by learning theory. However, the improvement over passive learning is not as dramatic as that achieved by machine active learning algorithms. To the best of our knowledge, this is the first quantitative study comparing human category learning in active versus passive settings.

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AAAI Conference 2007 Conference Paper

Humans Perform Semi-Supervised Classification Too

Xiaojin Zhu
Ruichen Qian

We explore the connections between machine learning and human learning in one form of semi-supervised classiﬁcation. 22 human subjects completed a novel 2class categorization task in which they were ﬁrst taught to categorize a single labeled example from each category, and subsequently were asked to categorize, without feedback, a large set of additional items. Stimuli were visually complex and unrecognizable shapes. The unlabeled examples were sampled from a bimodal distribution with modes appearing either to the left (leftshift condition) or right (right-shift condition) of the two labeled examples. Results showed that, although initial decision boundaries were near the middle of the two labeled examples, after exposure to the unlabeled examples, they shifted in different directions in the two groups. In this respect, the human behavior conformed well to the predictions of a Gaussian mixture model for semi-supervised learning. The human behavior differed from model predictions in other interesting respects, suggesting some fruitful avenues for future inquiry.

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