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Found 12 papers, 5 papers with code
Dynamical Survival Analysis with Controlled Latent States
We consider the task of learning individual-specific intensities of counting processes from a set of static variables and irregularly sampled time series.
On the Generalization and Approximation Capacities of Neural Controlled Differential Equations
Neural Controlled Differential Equations (NCDEs) are a state-of-the-art tool for supervised learning with irregularly sampled time series (Kidger, 2020).
Learning the Dynamics of Sparsely Observed Interacting Systems
We address the problem of learning the dynamics of an unknown non-parametric system linking a target and a feature time series.
SurvCaus : Representation Balancing for Survival Causal Inference
Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years.
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework
We then compare performances of all methods both in terms of risk prediction and variable selection, with a focus on the use of Elastic-Net regularization technique.
Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics
We introduce the binacox, a prognostic method to deal with the problem of detecting multiple cut-points per features in a multivariate setting where a large number of continuous features are available.
ConvSCCS: convolutional self-controlled case series model for lagged adverse event detection
With the increased availability of large databases of electronic health records (EHRs) comes the chance of enhancing health risks screening.
High-dimensional robust regression and outliers detection with SLOPE
Following a recent set of works providing methods for simultaneous robust regression and outliers detection, we consider in this paper a model of linear regression with individual intercepts, in a high-dimensional setting.
Binarsity: a penalization for one-hot encoded features in linear supervised learning
In each group of binary features coming from the one-hot encoding of a single raw continuous feature, this penalization uses total-variation regularization together with an extra linear constraint.
C-mix: a high dimensional mixture model for censored durations, with applications to genetic data
We introduce a mixture model for censored durations (C-mix), and develop maximum likelihood inference for the joint estimation of the time distributions and latent regression parameters of the model.
SGD with Variance Reduction beyond Empirical Risk Minimization
We introduce a doubly stochastic proximal gradient algorithm for optimizing a finite average of smooth convex functions, whose gradients depend on numerically expensive expectations.
Learning the intensity of time events with change-points
We prove that this leads to a sharp tuning of the convex relaxation of the segmentation prior, by stating oracle inequalities with fast rates of convergence, and consistency for change-points detection.
