Tackling new machine learning problems with neural networks always means optimizing numerous hyperparameters that define their structure and strongly impact their performances. In this work, we use a robust system conception approach to build explainable hyperparameters optimization. This approach is defined by the research of a parametrization of ...
With increasing data availability, causal effects can be evaluated across different data sets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the effect of the treatment from that of unwanted (confounding) co-occurring effects but they may suffer from un- representativeness, and thus lack external validity. On the ...
Expectiles form a family of risk measures that have recently gained interest over the more common value-at-risk or return levels, primarily due to their capability to be determined by the probabilities of tail values and magnitudes of realisations at once. However, a prevalent and ongoing challenge of expectile inference is the problem of uncertain...
Probabilistic record linkage is a process of combining data from different sources, when such data refer to common entities and identifying information is not available. Fellegi and Sunter proposed a probabilistic record linkage framework that takes into account multiple non-identifying information, but is limited to simple binary comparison betwee...
Objectives In modern professional life, mental health prevention and promotion have become a major challenge for decision-makers. Devising appropriate actions requires better understanding the role played by each work-related psychosocial factor (WPSF). The objective of this study was to present a relevant tool to hierarchise WPSFs that jointly tak...
Large or very large spatial (and spatio-temporal) datasets have become common place in many environmental and climate studies. These data are often collected in non-Euclidean spaces (such as the planet Earth) and they often present non-stationary anisotropies. This paper proposes a generic approach to model Gaussian Random Fields (GRFs) on compact ...
Gaussian processes have become essential for non-parametric function estimation and widely used in many fields like machine learning. In this paper, large scale Gaussian process regression (GPR) is investigated. This problem is related to the simulation of high dimensional Gaussian vectors truncated on the intersection of a set of hyperplanes. The ...
In the task of predicting spatio-temporal fields in environmental science, introducing models inspired by the physics of the underlying phenomena that are numerically efficient is of growing interest in spatial statistics. The size of space-time datasets calls for new numerical methods to efficiently process them. The SPDE (Stochastic Partial Diffe...
Purpose: To assess the incidence of undertriage in major trauma, its determinant, and association with mortality.Methods: A multicentre retrospective cohort study was conducted using data from a French regional trauma registry (2011-2017). All major trauma (Injury Severity Score ≥ 16) cases aged ≥ 18 years and managed by a physician-led mobile medi...
A wide variety of model explanation approaches have been proposed in recent years, all guided by very different rationales and heuristics. In this paper, we take a new route and cast interpretability as a statistical inference problem. We propose a general deep probabilistic model designed to produce interpretable predictions. The model parameters ...
