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Found 7 papers, 1 papers with code
Membership Inference Attacks via Adversarial Examples
In this paper, we develop a mean to measure the leakage of training data leveraging a quantity appearing as a proxy of the total variation of a trained model near its training samples.
Leveraging Adversarial Examples to Quantify Membership Information Leakage
The use of personal data for training machine learning systems comes with a privacy threat and measuring the level of privacy of a model is one of the major challenges in machine learning today.
Concentration bounds for the empirical angular measure with statistical learning applications
The angular measure on the unit sphere characterizes the first-order dependence structure of the components of a random vector in extreme regions and is defined in terms of standardized margins.
Feature Clustering for Support Identification in Extreme Regions
In the framework of multivariate Extreme Value Theory, a common characterization of extremes' dependence structure is the angular measure.
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation
The dominant approaches to text representation in natural language rely on learning embeddings on massive corpora which have convenient properties such as compositionality and distance preservation.
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On Binary Classification in Extreme Regions
In pattern recognition, a random label Y is to be predicted based upon observing a random vector X valued in $\mathbb{R}^d$ with d>1 by means of a classification rule with minimum probability of error.
