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…
Deep Manifold Traversal: Changing Labels with Convolutional Features
Jacob R. Gardner, Paul Upchurch, Matt J. Kusner +4
Many tasks in computer vision can be cast as a "label changing" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in or…
Compressing Convolutional Neural Networks
Wenlin Chen, James T. Wilson, Stephen Tyree +2
Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities o…
Compressing Neural Networks with the Hashing Trick
Wenlin Chen, James T. Wilson, Stephen Tyree +2
As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-in…
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…
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…