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Found 25 papers, 5 papers with code
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.
On Medians of (Randomized) Pairwise Means
Tournament procedures, recently introduced in Lugosi & Mendelson (2016), offer an appealing alternative, from a theoretical perspective at least, to the principle of Empirical Risk Minimization in machine learning.
A Statistical Learning View of Simple Kriging
The prediction rule being derived from a training spatial dataset: a single realization $X'$ of $X$, independent from those to be predicted, observed at $n\geq 1$ locations $\sigma_1,\; \ldots,\; \sigma_n$ in $S$.
Improving the quality control of seismic data through active learning
Each denoise step is then ideally followed by a quality control (QC) stage performed by means of human expertise.
Functional Anomaly Detection: a Benchmark Study
After an overview of the state-of-the-art and a visual-descriptive study, a variety of anomaly detection methods are compared.
Fighting Selection Bias in Statistical Learning: Application to Visual Recognition from Biased Image Databases
In practice, and especially when training deep neural networks, visual recognition rules are often learned based on various sources of information.
Individual Survival Curves with Conditional Normalizing Flows
Survival analysis, or time-to-event modelling, is a classical statistical problem that has garnered a lot of interest for its practical use in epidemiology, demographics or actuarial sciences.
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications
Motivated by a wide variety of applications, ranging from stochastic optimization to dimension reduction through variable selection, the problem of estimating gradients accurately is of crucial importance in statistics and learning theory.
Generalization Bounds in the Presence of Outliers: a Median-of-Means Study
In contrast to the empirical mean, the Median-of-Means (MoM) is an estimator of the mean $\theta$ of a square integrable r. v.
A Multiclass Classification Approach to Label Ranking
However, in a wide variety of situations, the task targeted may be more ambitious, consisting in sorting all the possible label values $y$ that may be assigned to $X$ by decreasing order of the posterior probability $\eta_y(X)=\mathbb{P}\{Y=y \mid X \}$.
Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints
We establish generalization bounds for scoring functions learned under such constraints, design practical learning algorithms and show the relevance our approach with numerical experiments on real and synthetic data.
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure
a statistical population may play a crucial role in this regard, anomalies corresponding to observations with 'small' depth.
Statistical Learning from Biased Training Samples
With the deluge of digitized information in the Big Data era, massive datasets are becoming increasingly available for learning predictive models.
Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning
The development of cluster computing frameworks has allowed practitioners to scale out various statistical estimation and machine learning algorithms with minimal programming effort.
Functional Isolation Forest
For the purpose of monitoring the behavior of complex infrastructures (e. g. aircrafts, transport or energy networks), high-rate sensors are deployed to capture multivariate data, generally unlabeled, in quasi continuous-time to detect quickly the occurrence of anomalies that may jeopardize the smooth operation of the system of interest.
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.
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach
Whereas most dimensionality reduction techniques (e. g. PCA, ICA, NMF) for multivariate data essentially rely on linear algebra to a certain extent, summarizing ranking data, viewed as realizations of a random permutation $\Sigma$ on a set of items indexed by $i\in \{1,\ldots,\; n\}$, is a great statistical challenge, due to the absence of vector space structure for the set of permutations $\mathfrak{S}_n$.
Autoencoding any Data through Kernel Autoencoders
This paper investigates a novel algorithmic approach to data representation based on kernel methods.
Profitable Bandits
We adapt and study three well-known strategies in this purpose, that were proved to be most efficient in other settings: kl-UCB, Bayes-UCB and Thompson Sampling.
Ranking Data with Continuous Labels through Oriented Recursive Partitions
This problem generalizes bi/multi-partite ranking to a certain extent and the task of finding optimal scoring functions s(x) can be naturally cast as optimization of a dedicated functional criterion, called the IROC curve here, or as maximization of the Kendall ${\tau}$ related to the pair (s(X), Y ).
Ranking Median Regression: Learning to Order through Local Consensus
In the probabilistic formulation of the 'Learning to Order' problem we propose, which extends the framework for statistical Kemeny ranking aggregation developped in \citet{CKS17}, this boils down to recovering conditional Kemeny medians of $\Sigma$ given $X$ from i. i. d.
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.
Mass Volume Curves and Anomaly Ranking
Here we formulate the issue of scoring anomalies as a M-estimation problem by means of a novel functional performance criterion, referred to as the Mass Volume curve (MV curve in short), whose optimal elements are strictly increasing transforms of the density almost everywhere on the support of the density.
On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability
The problem of predicting connections between a set of data points finds many applications, in systems biology and social network analysis among others.
