5 papers
Compact set-valued deep ensembling in multi-class classification
Kim-Dung Tran, Dang-Man Nguyen, Vu-Linh Nguyen +3
This paper tackles visible challenges in deep ensemble learning, where deep neural networks serve as ensemble members: training and storage burdens, and robustness of cautious (set…
SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors
Soundouss Messoudi, Sylvain Rousseau, Sébastien Destercke
Conformal Prediction (CP) provides robust uncertainty guarantees for predictive models, but is typically applied post hoc, which misaligns model training with the conformal goal of…
Robust Explanations Through Uncertainty Decomposition: A Path to Trustworthier AI
Chenrui Zhu, Louenas Bounia, Vu Linh Nguyen +2
Recent advancements in machine learning have emphasized the need for transparency in model predictions, particularly as interpretability diminishes when using increasingly complex…
Guaranteed prediction sets for functional surrogate models
Ander Gray, Vignesh Gopakumar, Sylvain Rousseau +1
We propose a method for obtaining statistically guaranteed prediction sets for functional machine learning methods: surrogate models which map between function spaces, motivated by…
Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition
Arthur Hoarau, Benjamin Quost, Sébastien Destercke +1
To generate accurate and reliable predictions, modern AI systems need to combine data from multiple modalities, such as text, images, audio, spreadsheets, and time series. Multi-mo…