Counterfactual Explanations for Time Series Forecasting

1 code implementation • 12 Oct 2023 • Zhendong Wang, Ioanna Miliou, Isak Samsten, Panagiotis Papapetrou

In this paper, we formulate the novel problem of counterfactual generation for time series forecasting, and propose an algorithm, called ForecastCF, that solves the problem by applying gradient-based perturbations to the original time series.

counterfactual Time Series +1

Early prediction of the risk of ICU mortality with Deep Federated Learning

1 code implementation • 1 Dec 2022 • Korbinian Randl, Núria Lladós Armengol, Lena Mondrejevski, Ioanna Miliou

In this study, we evaluate the ability of deep Federated Learning to predict the risk of Intensive Care Unit mortality at an early stage.

Federated Learning ICU Mortality

FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction

1 code implementation • 30 May 2022 • Lena Mondrejevski, Ioanna Miliou, Annaclaudia Montanino, David Pitts, Jaakko Hollmén, Panagiotis Papapetrou

Thus, the federated approach can be seen as a valid and privacy-preserving alternative to centralized machine learning for classifying ICU mortality when sharing sensitive patient data between hospitals is not possible.

BIG-bench Machine Learning Binary Classification +6

Understanding peacefulness through the world news

1 code implementation • 1 Jun 2021 • Vasiliki Voukelatou, Ioanna Miliou, Fosca Giannotti, Luca Pappalardo

Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers.

BIG-bench Machine Learning

Predicting seasonal influenza using supermarket retail records

1 code implementation • 8 Dec 2020 • Ioanna Miliou, Xinyue Xiong, Salvatore Rinzivillo, Qian Zhang, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi, Alessandro Vespignani

In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting.