Statistical Learning
Outlier-Robust Distributionally Robust Optimization via Unbalanced Optimal Transport
In this paper, we propose a DRO framework that relies on a new distance inspired by Unbalanced Optimal Transport (UOT).
Equivariant spatio-hemispherical networks for diffusion MRI deconvolution
Each voxel in a diffusion MRI (dMRI) image contains a spherical signal corresponding to the direction and strength of water diffusion in the brain.
The Many Faces of Optimal Weak-to-Strong Learning
's are real-valued weights depending on the accuracy of h
Rejection via Learning Density Ratios
Instead, we propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance.
Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models
During the CL of VLMs, we need not only to prevent the catastrophic forgetting on incrementally learned knowledge but also to preserve the zero-shot ability of VLMs.