The stochastic proximal point (SPP) methods have gained recent attention...
ℓ_0 constrained optimization is prevalent in machine learning,
particula...
The FedProx algorithm is a simple yet powerful distributed proximal poin...
Exponential generalization bounds with near-tight rates have recently be...
In this paper, we analyze the generalization performance of the Iterativ...
For an image query, unsupervised contrastive learning labels crops of th...
Meta-learning is a powerful paradigm for few-shot learning. Although wit...
The ℓ_0-constrained empirical risk minimization (ℓ_0-ERM) is a
promising...
The DANE algorithm is an approximate Newton method popularly used for
co...
Prevalent matrix completion theories reply on an assumption that the
loc...
Iterative Hard Thresholding (IHT) is a class of projected gradient desce...
Hard Thresholding Pursuit (HTP) is an iterative greedy selection procedu...
We investigate a generic problem of learning pairwise exponential family...
This paper studies the partial estimation of Gaussian graphical models f...
This paper considers the sparse eigenvalue problem, which is to extract
...