519 citations · 726 across the 9 of their papers we have counts for
9 papers
Gradient Regularized Budgeted Boosting
Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger +1
As machine learning transitions increasingly towards real world applications controlling the test-time cost of algorithms becomes more and more crucial. Recent work, such as the Gr…
Gradient Boosted Feature Selection
Zhixiang Eddie Xu, Gao Huang, Kilian Q. Weinberger +1
A feature selection algorithm should ideally satisfy four conditions: reliably extract relevant features; be able to identify non-linear feature interactions; scale linearly with t…
Compressed Support Vector Machines
Zhixiang Xu, Jacob R. Gardner, Stephen Tyree +1
Support vector machines (SVM) can classify data sets along highly non-linear decision boundaries because of the kernel-trick. This expressiveness comes at a price: During test-time…
An alternative text representation to TF-IDF and Bag-of-Words
Zhixiang, Xu, Minmin Chen +2
In text mining, information retrieval, and machine learning, text documents are commonly represented through variants of sparse Bag of Words (sBoW) vectors (e.g. TF-IDF). Although…
Cost-Sensitive Tree of Classifiers
Zhixiang Xu, Matt J. Kusner, Kilian Q. Weinberger +1
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 duri…
Distance Metric Learning for Kernel Machines
Zhixiang Xu, Kilian Q. Weinberger, Olivier Chapelle
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, a…