Search Results for author:
Found 8 papers, 5 papers with code
Compressed online Sinkhorn
The use of optimal transport (OT) distances, and in particular entropic-regularised OT distances, is an increasingly popular evaluation metric in many areas of machine learning and data science.
Smooth over-parameterized solvers for non-smooth structured optimization
Our main theoretical contribution connects gradient descent on this reformulation to a mirror descent flow with a varying Hessian metric.
Smooth Bilevel Programming for Sparse Regularization
Iteratively reweighted least square (IRLS) is a popular approach to solve sparsity-enforcing regression problems in machine learning.
Screening for Sparse Online Learning
In the realm of deterministic optimization, the sequence generated by iterative algorithms (such as proximal gradient descent) exhibit "finite activity identification", namely, they can identify the low-complexity structure in a finite number of iterations.
An off-the-grid approach to multi-compartment magnetic resonance fingerprinting
We propose a novel numerical approach to separate multiple tissue compartments in image voxels and to estimate quantitatively their nuclear magnetic resonance (NMR) properties and mixture fractions, given magnetic resonance fingerprinting (MRF) measurements.
Degrees of freedom for off-the-grid sparse estimation
Our main contribution is a proof of a continuous counterpart to this result for the Blasso.
On instabilities of deep learning in image reconstruction - Does AI come at a cost?
Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field.
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration
In this paper, we present a local convergence anal- ysis for a class of stochastic optimisation meth- ods: the proximal variance reduced stochastic gradient methods, and mainly focus on SAGA (Defazio et al., 2014) and Prox-SVRG (Xiao & Zhang, 2014).
