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Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
Machine unlearning has raised significant interest with the adoption of laws ensuring the "right to be forgotten".
Learning Provably Robust Estimators for Inverse Problems via Jittering
Deep neural networks provide excellent performance for inverse problems such as denoising. However, neural networks can be sensitive to adversarial or worst-case perturbations. This raises the question of whether such networks can be trained efficiently to be worst-case robust.
A Theory of Multimodal Learning
Human perception of the empirical world involves recognizing the diverse appearances, or'modalities', of underlying objects.
A Theory of Multimodal Learning
Human perception of the empirical world involves recognizing the diverse appearances, or'modalities', of underlying objects.
Credal Deep Ensembles for Uncertainty Quantification
This paper presents an innovative approach to classification tasks called Credal Deep Ensembles (CreDEs), ensembles of novel Credal-Set Neural Networks (CreNets), aiming to improve EU quantification in the framework of credal inference.