Search Results for author:
Found 40 papers, 1 papers with code
Meta-survey on outlier and anomaly detection
From this comprehensive collection, a subset of 56 papers that claim to be general surveys on outlier detection is selected using a snowball search technique to enhance field coverage.
Mixture of von Mises-Fisher distribution with sparse prototypes
We also introduce a new data set on financial reports and exhibit the benefits of our method for exploratory analysis.
Fast and fully-automated histograms for large-scale data sets
G-Enum histograms are a new fast and fully automated method for irregular histogram construction.
Challenges in anomaly and change point detection
On the one hand, the main concepts needed to understand the vast scientific literature on those subjects are introduced.
Model Based Co-clustering of Mixed Numerical and Binary Data
Co-clustering is a data mining technique used to extract the underlying block structure between the rows and columns of a data matrix.
Co-clustering based exploratory analysis of mixed-type data tables
Co-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as homogeneous blocks.
Federated Learning -- Methods, Applications and beyond
In recent years the applications of machine learning models have increased rapidly, due to the large amount of available data and technological progress. While some domains like web analysis can benefit from this with only minor restrictions, other fields like in medicine with patient data are strongerregulated.
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization.
Binary Diffing as a Network Alignment Problem via Belief Propagation
In this paper, we address the problem of finding a correspondence, or matching, between the functions of two programs in binary form, which is one of the most common task in binary diffing.
Improved Algorithm for the Network Alignment Problem with Application to Binary Diffing
In this paper, we present a novel algorithm to address the Network Alignment problem.
On the expressivity of bi-Lipschitz normalizing flows
An invertible function is bi-Lipschitz if both the function and its inverse have bounded Lipschitz constants.
Un modèle Bayésien de co-clustering de données mixtes
The proposed model infers an optimal segmentation of all variables then performs a co-clustering by minimizing a Bayesian model selection cost function.
The State of the Art in Integrating Machine Learning into Visual Analytics
Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning.
Block modelling in dynamic networks with non-homogeneous Poisson processes and exact ICL
We develop a model in which interactions between nodes of a dynamic network are counted by non homogeneous Poisson processes.
Discovering Patterns in Time-Varying Graphs: A Triclustering Approach
This paper introduces a novel technique to track structures in time varying graphs.
Mean Absolute Percentage Error for regression models
We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models.
Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks
The stochastic block model (SBM) is a flexible probabilistic tool that can be used to model interactions between clusters of nodes in a network.
Co-Clustering Network-Constrained Trajectory Data
Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities.
Lasso based feature selection for malaria risk exposure prediction
In life sciences, the experts generally use empirical knowledge to recode variables, choose interactions and perform selection by classical approach.
Study of a bias in the offline evaluation of a recommendation algorithm
Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles.
A Study of the Spatio-Temporal Correlations in Mobile Calls Networks
In this article, we introduce a practical analysis of a large database from a telecommunication operator.
Sélection de variables par le GLM-Lasso pour la prédiction du risque palustre
Because the target variable is account variable and the lasso estimators are biased, variables selected by lasso are debiased by a GLM and used to predict the distribution of the main vector of malaria which is Anopheles.
Empirical risk minimization is consistent with the mean absolute percentage error
We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models.
Modelling time evolving interactions in networks through a non stationary extension of stochastic block models
To overcome this limitation, we propose a partition of the whole time horizon, in which interactions are observed, and develop a non stationary extension of the SBM, allowing to simultaneously cluster the nodes in a network along with fixed time intervals in which the interactions take place.
Graphs in machine learning: an introduction
In this paper, we give an introduction to some methods relying on graphs for learning.
Search Strategies for Binary Feature Selection for a Naive Bayes Classifier
We compare in this paper several feature selection methods for the Naive Bayes Classifier (NBC) when the data under study are described by a large number of redundant binary indicators.
Using the Mean Absolute Percentage Error for Regression Models
We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models.
Reducing offline evaluation bias of collaborative filtering algorithms
Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles.
Exact ICL maximization in a non-stationary time extension of the latent block model for dynamic networks
The latent block model (LBM) is a flexible probabilistic tool to describe interactions between node sets in bipartite networks, but it does not account for interactions of time varying intensity between nodes in unknown classes.
Country-scale Exploratory Analysis of Call Detail Records through the Lens of Data Grid Models
Call Detail Records (CDRs) are data recorded by telecommunications companies, consisting of basic informations related to several dimensions of the calls made through the network: the source, destination, date and time of calls.
Interpretable Aircraft Engine Diagnostic via Expert Indicator Aggregation
Detecting early signs of failures (anomalies) in complex systems is one of the main goal of preventive maintenance.
Anomaly Detection Based on Indicators Aggregation
Automatic anomaly detection is a major issue in various areas.
A Methodology for the Diagnostic of Aircraft Engine Based on Indicators Aggregation
Aircraft engine manufacturers collect large amount of engine related data during flights.
Reducing Offline Evaluation Bias in Recommendation Systems
Recommendation systems have been integrated into the majority of large online systems.
Anomaly Detection Based on Aggregation of Indicators
Automatic anomaly detection is a major issue in various areas.
How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?
In numerous applicative contexts, data are too rich and too complex to be represented by numerical vectors.
Nonparametric Hierarchical Clustering of Functional Data
In this paper, we deal with the problem of curves clustering.
Graph-Based Approaches to Clustering Network-Constrained Trajectory Data
In this paper, we present an approach to clustering such network-constrained trajectory data.
Regularization in Relevance Learning Vector Quantization Using l one Norms
We propose in this contribution a method for l one regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance profiles.
A bag-of-paths framework for network data analysis
This probability captures a notion of relatedness between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths.
