output
20062025
most citedClustered Multi-Task Learning: A Convex Formulation

285 citations

16 papers

stat.AP2025

A note on a resampling procedure for estimating the density at a given quantile

Beatriz Farah, Aurélien Latouche, Olivier Bouaziz

In this paper we refine the procedure proposed by Lin et al. (2015) to estimate the density at a given quantile based on a resampling method. The approach consists on generating mu…

cs.CV2025★ 2 cited

MIPHEI-ViT: Multiplex Immunofluorescence Prediction from H&E Images using ViT Foundation Models

Guillaume Balezo, Roger Trullo, Albert Pla Planas +2

Histopathological analysis is a cornerstone of cancer diagnosis, with Hematoxylin and Eosin (H&E) staining routinely acquired for every patient to visualize cell morphology and tis…

q-bio.QM2025★ 13 cited

In silico clinical trials in drug development: a systematic review

Bohua Chen, Lucia Chantal Schneider, Christian Röver +15

In the context of clinical research, computational models have received increasing attention over the past decades. In this systematic review, we aimed to provide an overview of th…

eess.IV2024★ 2 cited

Interpretable Embeddings for Segmentation-Free Single-Cell Analysis in Multiplex Imaging

Simon Gutwein, Daria Lazic, Thomas Walter +2

Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution, providing valuable insights int…

stat.ML2024★ 33 cited

Supervised Multiple Kernel Learning approaches for multi-omics data integration

Mitja Briscik, Gabriele Tazza, Marie-Agnes Dillies +2

Advances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an i…

cs.AI2023

MultiZenoTravel: a Tunable Benchmark for Multi-Objective Planning with Known Pareto Front

Alexandre Quemy, Marc Schoenauer, Johann Dreo

Multi-objective AI planning suffers from a lack of benchmarks exhibiting known Pareto Fronts. In this work, we propose a tunable benchmark generator, together with a dedicated solv…