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Found 13 papers, 2 papers with code
Sharp error bounds for imbalanced classification: how many examples in the minority class?
When dealing with imbalanced classification data, reweighting the loss function is a standard procedure allowing to equilibrate between the true positive and true negative rates within the risk measure.
Regular Variation in Hilbert Spaces and Principal Component Analysis for Functional Extremes
Motivated by the increasing availability of data of functional nature, we develop a general probabilistic and statistical framework for extremes of regularly varying random elements $X$ in $L^2[0, 1]$.
On Regression in Extreme Regions
The statistical learning problem consists in building a predictive function $\hat{f}$ based on independent copies of $(X, Y)$ so that $Y$ is approximated by $\hat{f}(X)$ with minimum (squared) error.
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.
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.
Ranked #3 on
Sentiment Analysis
on Yelp Binary classification
A Multivariate Extreme Value Theory Approach to Anomaly Clustering and Visualization
In a wide variety of situations, anomalies in the behaviour of a complex system, whose health is monitored through the observation of a random vector X = (X1,.
Principal Component Analysis for Multivariate Extremes
Within the statistical learning framework of empirical risk minimization, our main focus is to analyze the squared reconstruction error for the exceedances over large radial thresholds.
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.
Max K-armed bandit: On the ExtremeHunter algorithm and beyond
This paper is devoted to the study of the max K-armed bandit problem, which consists in sequentially allocating resources in order to detect extreme values.
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking
Extremes play a special role in Anomaly Detection.
Sparsity in Multivariate Extremes with Applications to Anomaly Detection
Capturing the dependence structure of multivariate extreme events is a major concern in many fields involving the management of risks stemming from multiple sources, e. g. portfolio monitoring, insurance, environmental risk management and anomaly detection.
On Anomaly Ranking and Excess-Mass Curves
Extensions to the multivariate setting are far from straightforward and it is precisely the main purpose of this paper to introduce a novel and convenient (functional) criterion for measuring the performance of a scoring function regarding the anomaly ranking task, referred to as the Excess-Mass curve (EM curve).
