Leveraging “chain-of-thought (CoT)” reasoning to elicit rapid and precis...
The integration of external personalized context information into
docume...
Bilevel optimization has recently regained interest owing to its applica...
Recently, min-max optimization problems have received increasing attenti...
Due to the significant computational challenge of training large-scale g...
Many real-world problems not only have complicated nonconvex functional
...
The graph neural network (GNN) models have presented impressive achievem...
In recent years, decentralized bilevel optimization problems have receiv...
Nonconvex constrained optimization problems can be used to model a numbe...
Current deep neural networks (DNNs) are vulnerable to adversarial attack...
This paper is the first to propose a generic min-max bilevel multi-objec...
In this paper, we investigate the performance of a practical aggregated
...
Multiple views of data, both naturally acquired (e.g., image and audio) ...
Deep neural networks have been shown as a class of useful tools for
addr...
XGBoost is one of the most widely used machine learning models in the
in...
Large-scale distributed training of Deep Neural Networks (DNNs) on
state...
In this paper, we consider a common unicast beamforming network where Al...
Adversarial examples causing evasive predictions are widely used to eval...
Data privacy and protection is a crucial issue for any automatic speech
...
Model-agnostic meta-learning (MAML) effectively meta-learns an initializ...
We propose a new randomized Bregman (block) coordinate descent
(RBCD) me...
Distributed learning has become a critical enabler of the massively conn...
In the applications of signal processing and data analytics, there is a ...
Federated learning opens a number of research opportunities due to its h...
The online meta-learning framework is designed for the continual lifelon...
Many modern large-scale machine learning problems benefit from decentral...
In this paper, we study the problem of constrained robust (min-max)
opti...
This paper proposes low-complexity algorithms for finding approximate
se...
In this paper, we investigate deep learning (DL)-enabled signal demodula...
The min-max problem, also known as the saddle point problem, is a class ...
A study on power market price forecasting by deep learning is presented....
The alternating gradient descent (AGD) is a simple but popular algorithm...
Symmetric nonnegative matrix factorization (SymNMF) has important
applic...