Adversarial attacks are usually expressed in terms of a gradient-based
o...
Most of the existing works on provable guarantees for low-rank matrix
co...
We introduce a Python open-source library for 𝒳-armed bandit and
online ...
We analyze a practical algorithm for sparse PCA on incomplete and noisy ...
This work establishes the first framework of federated 𝒳-armed
bandit, w...
We study a practical algorithm for sparse principal component analysis (...
The recent proposed self-supervised learning (SSL) approaches successful...
In this paper, we consider the use of large-scale genomics data for trea...
The Ensemble Kalman Filter (EnKF) has achieved great successes in data
a...
Deep learning has been the engine powering many successes of data scienc...
Sparse deep learning aims to address the challenge of huge storage
consu...
We propose a variational Bayesian (VB) procedure for high-dimensional li...
Bayesian deep learning offers a principled way to address many issues
co...
Modern machine learning and deep learning models are shown to be vulnera...
Shrinkage prior are becoming more and more popular in Bayesian modeling ...
We consider a data corruption scenario in the classical k Nearest Neighb...
Stochastic gradient Markov chain Monte Carlo (MCMC) algorithms have rece...
Sparse deep neural network (DNN) has drawn much attention in recent stud...
The over-parameterized models attract much attention in the era of data
...
Shrinkage prior has gained great successes in many data analysis, howeve...
In the analysis of high dimensional regression models, there are two
imp...
In the era of deep learning, understanding over-fitting phenomenon becom...
During the past decade, shrinkage priors have received much attention in...