Search Results for author:
Found 15 papers, 3 papers with code
Classification Tree-based Active Learning: A Wrapper Approach
A classification tree constructed on an initial set of labeled samples is considered to decompose the space into low-entropy regions.
On the Fly Detection of Root Causes from Observed Data with Application to IT Systems
This paper introduces a new structural causal model tailored for representing threshold-based IT systems and presents a new algorithm designed to rapidly detect root causes of anomalies in such systems.
Identifiability of total effects from abstractions of time series causal graphs
We study the problem of identifiability of the total effect of an intervention from observational time series only given an abstraction of the causal graph of the system.
Pool-Based Active Learning with Proper Topological Regions
Machine learning methods usually rely on large sample size to have good performance, while it is difficult to provide labeled set in many applications.
Case Studies of Causal Discovery from IT Monitoring Time Series
Despite its potential benefits, applying causal discovery algorithms on IT monitoring data poses challenges, due to the complexity of the data.
Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms
In the second class, a constraint-based strategy is applied to identify a skeleton, which is then oriented using a noise-based strategy.
Inferring extended summary causal graphs from observational time series
This study addresses the problem of learning an extended summary causal graph on time series.
Self-Training: A Survey
Semi-supervised algorithms aim to learn prediction functions from a small set of labeled observations and a large set of unlabeled observations.
Multi-class Probabilistic Bounds for Self-learning
First, we derive a transductive bound over the risk of the multi-class majority vote classifier.
Entropy-based Discovery of Summary Causal Graphs in Time Series
This study addresses the problem of learning a summary causal graph on time series with potentially different sampling rates.
Smooth And Consistent Probabilistic Regression Trees
We propose here a generalization of regression trees, referred to as Probabilistic Regression (PR) trees, that adapt to the smoothness of the prediction function relating input and output variables while preserving the interpretability of the prediction and being robust to noise.
Semi-supervised Wrapper Feature Selection by Modeling Imperfect Labels
In this paper, we propose a new wrapper feature selection approach with partially labeled training examples where unlabeled observations are pseudo-labeled using the predictions of an initial classifier trained on the labeled training set.
Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees
Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies.
Nonlinear network-based quantitative trait prediction from transcriptomic data
In this paper, we present a novel approach designed to predict quantitative trait from transcriptomic data, taking into account the heterogeneity in biological samples and the hidden gene regulatory networks underlying different biological mechanisms.
Block-diagonal covariance selection for high-dimensional Gaussian graphical models
Based on the block-diagonal structure of the covariance matrix, the estimation problem is divided into several independent problems: subsequently, the network of dependencies between variables is inferred using the graphical lasso algorithm in each block.
