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Stephen M. Chu

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2 papers

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ICML Conference 2016 Conference Paper

Low-Rank Matrix Approximation with Stability

Dongsheng Li 0002
Chao Chen 0016
Qin Lv
Junchi Yan
Li Shang 0001
Stephen M. Chu

Low-rank matrix approximation has been widely adopted in machine learning applications with sparse data, such as recommender systems. However, the sparsity of the data, incomplete and noisy, introduces challenges to the algorithm stability – small changes in the training data may significantly change the models. As a result, existing low-rank matrix approximation solutions yield low generalization performance, exhibiting high error variance on the training dataset, and minimizing the training error may not guarantee error reduction on the testing dataset. In this paper, we investigate the algorithm stability problem of low-rank matrix approximations. We present a new algorithm design framework, which (1) introduces new optimization objectives to guide stable matrix approximation algorithm design, and (2) solves the optimization problem to obtain stable low-rank approximation solutions with good generalization performance. Experimental results on real-world datasets demonstrate that the proposed work can achieve better prediction accuracy compared with both state-of-the-art low-rank matrix approximation methods and ensemble methods in recommendation task.

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IJCAI Conference 2016 Conference Paper

On Modeling and Predicting Individual Paper Citation Count over Time

Shuai Xiao
Junchi Yan
Changsheng Li
Bo Jin
Xiangfeng Wang
Xiaokang Yang
Stephen M. Chu
Hongyuan Zha

Evaluating a scientist's past and future potential impact is key in decision making concerning with recruitment and funding, and is increasingly linked to publication citation count. Meanwhile, timely identifying those valuable work with great potential before they receive wide recognition and become highly cited Abstracts is both useful for readers and authors in many regards. We propose a method for predicting the citation counts of individual publications, over an arbitrary time period. Our approach explores paper-specific covariates, and a point process model to account for the aging effect and triggering role of recent citations, through which Abstracts lose and gain their popularity, respectively. Empirical results on the Microsoft Academic Graph data suggests that our model can be useful for both prediction and interpretability.

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