2.3k citations · 8.3k across the 36 of their papers we have counts for
6 papers · 1 filter
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications
Nicholas Carlini, Úlfar Erlingsson, Nicolas Papernot
We develop techniques to quantify the degree to which a given (training or testing) example is an outlier in the underlying distribution. We evaluate five methods to score examples…
Improving Differentially Private Models with Active Learning
Zhengli Zhao, Nicolas Papernot, Sameer Singh +2
Broad adoption of machine learning techniques has increased privacy concerns for models trained on sensitive data such as medical records. Existing techniques for training differen…
High Accuracy and High Fidelity Extraction of Neural Networks
Matthew Jagielski, Nicholas Carlini, David Berthelot +2
In a model extraction attack, an adversary steals a copy of a remotely deployed machine learning model, given oracle prediction access. We taxonomize model extraction attacks aroun…
MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot, Nicholas Carlini, Ian Goodfellow +3
Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current d…
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness
Jörn-Henrik Jacobsen, Jens Behrmannn, Nicholas Carlini +2
Adversarial examples are malicious inputs crafted to cause a model to misclassify them. Their most common instantiation, "perturbation-based" adversarial examples introduce changes…
On Evaluating Adversarial Robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot +6
Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that wi…