2.4k citations · 12.3k across the 115 of their papers we have counts for
6 papers · 2 filters
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Tao Yu, Shengyuan Hu, Chuan Guo +2
Natural images are virtually surrounded by low-density misclassified regions that can be efficiently discovered by gradient-guided search --- enabling the generation of adversarial…
Simple Black-box Adversarial Attacks
Chuan Guo, Jacob R. Gardner, Yurong You +2
We propose an intriguingly simple method for the construction of adversarial images in the black-box setting. In constrast to the white-box scenario, constructing black-box adversa…
Exact Gaussian Processes on a Million Data Points
Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner +3
Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows with the available data. However, computational constraints with standard inference procedur…
Simplifying Graph Convolutional Networks
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza +3
Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. GCNs derive i…
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…