Statistical Learning
Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling Peter Halmos
We leverage these advantages to derive a new algorithm Factor Relaxation with Latent Coupling (FRLC), which uses coordinate mirror descent to compute the LC factorization.
Implicit Bias of Mirror Flow on Separable Data
When the potential is separable, a simple formula allows to compute this function.
UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models
Specifically, we observe that CLIP's visual encoder tends to
Improved Particle Approximation Error for Mean Field Neural Networks
Recent works (Chen et al., 2022; Suzuki et al., 2023b) have demonstrated In this work, we improve the dependence on logarithmic Sobolev inequality (LSI) constants in their particle approximation errors which can exponentially deteriorate with the regularization coefficient. One may consider adding Gaussian noise to the gradient descent to make the method more stable.
