519 citations · 726 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
The Greedy Miser: Learning under Test-time Budgets
Zhixiang Xu, Kilian Weinberger, Olivier Chapelle
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 run…
Marginalized Denoising Autoencoders for Domain Adaptation
Minmin Chen, Zhixiang Xu, Kilian Weinberger +1
Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently, they have attained record accuracy on standard bench…
Rapid Feature Learning with Stacked Linear Denoisers
Zhixiang Eddie Xu, Kilian Q. Weinberger, Fei Sha
We investigate unsupervised pre-training of deep architectures as feature generators for "shallow" classifiers. Stacked Denoising Autoencoders (SdA), when used as feature pre-proce…