Zhixiang Eddie Xu

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Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently, they have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are(More)
As machine learning algorithms enter applications in industrial settings, there is increased interest in controlling their cpu-time during testing. The cpu-time consists of the running time of the algorithm and the extraction time of the features. The latter can vary drastically when the feature set is diverse. In this paper, we propose an algorithm, the(More)
Recently, machine learning algorithms have successfully entered large-scale real-world industrial applications (e.g. search engines and email spam filters). Here, the CPU cost during test-time must be budgeted and accounted for. In this paper, we address the challenge of balancing the test-time cost and the classifier accuracy in a principled fashion. The(More)
Recent work in metric learning has significantly improved the state-of-the-art in k-nearest neighbor classification. Support vector machines (SVM), particularly with RBF kernels, are amongst the most popular classification algorithms that uses distance metrics to compare examples. This paper provides an empirical analysis of the efficacy of three of the(More)
During the past decade, machine learning algorithms have become commonplace in large-scale real-world industrial applications. In these settings, the computation time to train and test machine learning algorithms is a key consideration. At training-time the algorithms must scale to very large data set sizes. At testing-time, the cost of feature extraction(More)
Machine learning algorithms are increasingly used in large-scale industrial settings. Here, the operational cost during test-time has to be taken into account when an algorithm is designed. This operational cost is affected by the average running time and the computation time required for feature extraction. When a diverse set of features is used, the(More)
Bayesian optimization is a powerful framework for minimizing expensive objective functions while using very few function evaluations. It has been successfully applied to a variety of problems, including hyperparameter tuning and experimental design. However, this framework has not been extended to the inequality-constrained optimization setting,(More)
Machine learning algorithms have successfully entered industry through many real-world applications (e.g. , search engines and product recommendations). In these applications, the test-time CPU cost must be budgeted and accounted for. In this paper, we examine two main components of the test-time CPU cost, classifier evaluation cost and feature extraction(More)
Vemurafenib (PLX4032), a selective inhibitor of Braf, has been approved by the US Food and Drug Administration for the treatment of unresectable or metastatic melanoma in patients with Braf(V600E) mutations. Many patients treated with vemurafenib initially display dramatic improvement, with decreases in both risk of death and tumor progression. Acquired(More)
Studies have suggested that the clock regulator PER2 is a tumour suppressor. A cancer network involving PER2 raises the possibility that some tumour suppressors are directly involved in the mammalian clock. Here, we show that the tumour suppressor promyelocytic leukaemia (PML) protein is a circadian clock regulator and can physically interact with PER2. In(More)