Over the past few years, significant advancements have been made in the field of machine
learning (ML) to address resource management, interference management, autonomy, and …

In recent years, various machine learning (ML) solutions have been developed to solve
resource management, interference management, autonomy, and decision-making …

We propose a new framework for reasoning about generalization in deep learning. The core
idea is to couple the Real World, where optimizers take stochastic gradient steps on the …

Owing to the fast deployment of distributed energy resources (DERs) and the further
development of demand-side management, small agents in electricity markets are becoming …

Participants in electricity markets are becoming more proactive owing to the fast deployment
of distributed energy resources (DERs) and the further development of demand-side …

We study the problem of online learning with non-convex losses, where the learner has
access to an offline optimization oracle. We show that the classical Follow the Perturbed …

We investigate online nonconvex optimization from a local regret minimization perspective.
Previous studies along this line implicitly required the access to sufficient gradient oracles at …

We study {\it model reusability evaluation}(MRE) for source pre-trained models: evaluating
their transfer learning performance to new target tasks. In special, we focus on the setting …

We present the online Newton's method, a single-step second-order method for online
nonconvex optimization. We analyze its performance and obtain a dynamic regret bound …

The development of large-scale image-captioning datasets is expensive, while the
abundance of unpaired images and text corpus can potentially help reduce the efforts of …