Hugo Yèche

According to our database, Hugo Yèche authored at least 10 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Dynamic Survival Analysis for Early Event Prediction.

Hugo Yèche, Manuel Burger, Dinara Veshchezerova, Gunnar Rätsch

CoRR, 2024

2023

Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding.

Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz

CoRR, 2023

Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference.

Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin

CoRR, 2023

On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series.

Rita Kuznetsova, Alizée Pace, Manuel Burger, Hugo Yèche, Gunnar Rätsch

Proceedings of the Machine Learning for Health, 2023

Temporal Label Smoothing for Early Event Prediction.

Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova

Proceedings of the International Conference on Machine Learning, 2023

2022

On the Importance of Clinical Notes in Multi-modal Learning for EHR Data.

Severin Husmann, Hugo Yèche, Gunnar Rätsch, Rita Kuznetsova

CoRR, 2022

Temporal Label Smoothing for Early Prediction of Adverse Events.

Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova

CoRR, 2022

2021

HiRID-ICU-Benchmark - A Comprehensive Machine Learning Benchmark on High-resolution ICU Data.

Hugo Yèche, Rita Kuznetsova, Marc Zimmermann, Matthias Hüser, Xinrui Lyu, Martin Faltys, Gunnar Rätsch

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Neighborhood Contrastive Learning Applied to Online Patient Monitoring.

Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch

Proceedings of the 38th International Conference on Machine Learning, 2021

2019

UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics.

Hugo Yèche, Justin Harrison, Tess Berthier

Proceedings of the Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support, 2019