This paper introduces a new Convolutional Neural Network (ConvNet)
archi...
As a fundamental aspect of human life, two-person interactions contain
m...
The main challenge of offline reinforcement learning, where data is limi...
We study the convergence behavior of the celebrated temporal-difference ...
New network architectures, such as the Internet of Things (IoT), 5G, and...
Resource limitations make it hard to provide all students with one of th...
Tremendous efforts have been devoted to pedestrian trajectory prediction...
We present a method to formulate algorithm discovery as program search, ...
Multi-server Federated learning (FL) has been considered as a promising
...
Recently, adversarial machine learning attacks have posed serious securi...
Universal domain adaptation (UniDA) is a general unsupervised domain
ada...
Offline policy optimization could have a large impact on many real-world...
Federated Learning (FL) is a promising framework for performing
privacy-...
We study reinforcement learning (RL) in settings where observations are
...
In this paper, we propose a generalized state-dependent channel modeling...
Tiny objects, frequently appearing in practical applications, have weak
...
With the proliferation of IoT devices, researchers have developed a vari...
Federated learning (FL) provides a high efficient decentralized machine
...
Federated Learning (FL) has been considered as an appealing framework to...
Integrated sensing and communication (ISAC) emerges as a new design para...
Mobile telepresence robots (MTRs) allow people to navigate and interact ...
With the emerging of 360-degree image/video, augmented reality (AR) and
...
We revisit the traditional framework of wireless secret key generation, ...
We present NetReduce, a novel RDMA-compatible in-network reduction
archi...
Batch reinforcement learning (RL) is important to apply RL algorithms to...
Off-policy evaluation in reinforcement learning offers the chance of usi...
Deep hashing methods have been proved to be effective and efficient for
...
While often stated as an instance of the likelihood ratio trick [Rubinst...
We establish a connection between the importance sampling estimators
typ...
We consider a model-based approach to perform batch off-policy evaluatio...
We study the problem of off-policy policy optimization in Markov decisio...
In this paper, we propose a light reflection based face anti-spoofing me...
In this work, we consider the problem of estimating a behaviour policy f...
Efficient exploration is one of the key challenges for reinforcement lea...
We study the problem of off-policy policy evaluation (OPPE) in RL. In
co...