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Found 29 papers, 7 papers with code
On Ranking-based Tests of Independence
In this paper we develop a novel nonparametric framework to test the independence of two random variables $\mathbf{X}$ and $\mathbf{Y}$ with unknown respective marginals $H(dx)$ and $G(dy)$ and joint distribution $F(dx dy)$, based on {\it Receiver Operating Characteristic} (ROC) analysis and bipartite ranking.
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
As the issue of robustness in AI systems becomes vital, statistical learning techniques that are reliable even in presence of partly contaminated data have to be developed.
Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition
The ROC curve is the major tool for assessing not only the performance but also the fairness properties of a similarity scoring function.
Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model
In spite of the high performance and reliability of deep learning algorithms in a wide range of everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against specific subgroups of the population (e. g. gender, ethnicity).
Ranked #1 on
Face Verification
on LFW
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
The concept of median/consensus has been widely investigated in order to provide a statistical summary of ranking data, i. e. realizations of a random permutation $\Sigma$ of a finite set, $\{1,\; \ldots,\; n\}$ with $n\geq 1$ say.
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics
The ability to collect and store ever more massive databases has been accompanied by the need to process them efficiently.
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis
Because it determines a center-outward ordering of observations in $\mathbb{R}^d$ with $d\geq 2$, the concept of statistical depth permits to define quantiles and ranks for multivariate data and use them for various statistical tasks (e. g. inference, hypothesis testing).
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.
Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite Ranking
The ROC curve is the gold standard for measuring the performance of a test/scoring statistic regarding its capacity to discriminate between two statistical populations in a wide variety of applications, ranging from anomaly detection in signal processing to information retrieval, through medical diagnosis.
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions
a probability distribution or a data set.
Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling
We consider statistical learning problems, when the distribution $P'$ of the training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution $P$ involved in the risk one seeks to minimize (referred to as the test distribution) but is still defined on the same measurable space as $P$ and dominates it.
Weighted Empirical Risk Minimization: Transfer Learning based on Importance Sampling
We consider statistical learning problems, when the distribution $P'$ of the training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution $P$ involved in the risk one seeks to minimize (referred to as the \textit{test distribution}) but is still defined on the same measurable space as $P$ and dominates it.
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,.
On Tree-based Methods for Similarity Learning
In many situations, the choice of an adequate similarity measure or metric on the feature space dramatically determines the performance of machine learning methods.
Empirical Risk Minimization under Random Censorship: Theory and Practice
As ignoring censorship in the risk computation may clearly lead to a severe underestimation of the target duration and jeopardize prediction, we propose to consider a plug-in estimate of the true risk based on a Kaplan-Meier estimator of the conditional survival function of the censorship $C$ given $X$, referred to as Kaplan-Meier risk, in order to perform empirical risk minimization.
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization
In this paper, similarity learning is investigated from the perspective of pairwise bipartite ranking, where the goal is to rank the elements of a database by decreasing order of the probability that they share the same label with some query data point, based on the similarity scores.
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
In decentralized networks (of sensors, connected objects, etc.
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking
Extremes play a special role in Anomaly Detection.
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk
In many learning problems, ranging from clustering to ranking through metric learning, empirical estimates of the risk functional consist of an average over tuples (e. g., pairs or triplets) of observations, rather than over individual observations.
Extending Gossip Algorithms to Distributed Estimation of U-Statistics
Efficient and robust algorithms for decentralized estimation in networks are essential to many distributed systems.
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).
Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics
In a wide range of statistical learning problems such as ranking, clustering or metric learning among others, the risk is accurately estimated by $U$-statistics of degree $d\geq 1$, i. e. functionals of the training data with low variance that take the form of averages over $k$-tuples.
Survey schemes for stochastic gradient descent with applications to M-estimation
In certain situations that shall be undoubtedly more and more common in the Big Data era, the datasets available are so massive that computing statistics over the full sample is hardly feasible, if not unfeasible.
Online Matrix Completion Through Nuclear Norm Regularisation
It is the main goal of this paper to propose a novel method to perform matrix completion on-line.
Functional Bipartite Ranking: a Wavelet-Based Filtering Approach
It is the main goal of this article to address the bipartite ranking issue from the perspective of functional data analysis (FDA).
An SIR Graph Growth Model for the Epidemics of Communicable Diseases
It is the main purpose of this paper to introduce a graph-valued stochastic process in order to model the spread of a communicable infectious disease.
Learning Reputation in an Authorship Network
The idea is to use Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) to perform topic modelling in order to find authors who have worked in a query field.
Efficient Eigen-updating for Spectral Graph Clustering
Namely, the method promoted in this article can be viewed as an incremental eigenvalue solution for the spectral clustering method described by Ng.
