Search Results for author:
Found 16 papers, 8 papers with code
Length of Stay prediction for Hospital Management using Domain Adaptation
In this study, we predict early hospital LoS at the granular level of admission units by applying domain adaptation to leverage information learned from a potential source domain.
Duality in Multi-View Restricted Kernel Machines
We propose a unifying setting that combines existing restricted kernel machine methods into a single primal-dual multi-view framework for kernel principal component analysis in both supervised and unsupervised settings.
Client Recruitment for Federated Learning in ICU Length of Stay Prediction
In this work, we address the step prior to the initiation of a federated network for model training, client recruitment.
Multi-view Kernel PCA for Time series Forecasting
In this paper, we propose a kernel principal component analysis model for multi-variate time series forecasting, where the training and prediction schemes are derived from the multi-view formulation of Restricted Kernel Machines.
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks
A generative model may overlook underrepresented modes that are less frequent in the empirical data distribution.
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks
However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i. e. the generative models not being able to sample from the entire probability distribution.
The Bures Metric for Generative Adversarial Networks
However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i. e. the generative models not being able to sample from the entire probability distribution.
Applicability and interpretation of the deterministic weighted cepstral distance
In this way, we provide a purely data-driven way to assess different underlying dynamics of input/output signal pairs, without the need for any system identification step.
A time series distance measure for efficient clustering of input output signals by their underlying dynamics
The first class of methods employs a distance measure on time series (e. g. Euclidean, Dynamic Time Warping) and a clustering technique (e. g. k-means, k-medoids, hierarchical clustering) to find natural groups in the dataset.
Building Classifiers to Predict the Start of Glucose-Lowering Pharmacotherapy Using Belgian Health Expenditure Data
We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers.
Assessing binary classifiers using only positive and unlabeled data
We provide theoretical bounds on the quality of our estimates, illustrate the importance of estimating the fraction of positives in the unlabeled set and demonstrate empirically that we are able to reliably estimate ROC and PR curves on real data.
Hyperparameter Search in Machine Learning
We introduce the hyperparameter search problem in the field of machine learning and discuss its main challenges from an optimization perspective.
Easy Hyperparameter Search Using Optunity
Optunity is a free software package dedicated to hyperparameter optimization.
EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines
EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models.
Fast Prediction with SVM Models Containing RBF Kernels
We present an approximation scheme for support vector machine models that use an RBF kernel.
A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models
The included benchmark comprises three settings with increasing label noise: (i) fully supervised, (ii) PU learning and (iii) PU learning with false positives.
