2.4k citations · 12.3k across the 115 of their papers we have counts for
6 papers · 1 filter
Private Causal Inference
Matt J. Kusner, Yu Sun, Karthik Sridharan +1
Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from stati…
Differentially Private Bayesian Optimization
Matt J. Kusner, Jacob R. Gardner, Roman Garnett +1
Bayesian optimization is a powerful tool for fine-tuning the hyper-parameters of a wide variety of machine learning models. The success of machine learning has led practitioners in…
Image Data Compression for Covariance and Histogram Descriptors
Matt J. Kusner, Nicholas I. Kolkin, Stephen Tyree +1
Covariance and histogram image descriptors provide an effective way to capture information about images. Both excel when used in combination with special purpose distance metrics.…
A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing
Quan Zhou, Wenlin Chen, Shiji Song +3
The past years have witnessed many dedicated open-source projects that built and maintain implementations of Support Vector Machines (SVM), parallelized for GPU, multi-core CPUs an…
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…