258 citations · 628 across the 13 of their papers we have counts for
14 papers
Multi-Agent Trust Region Policy Optimization
Hepeng Li, Haibo He
We extend trust region policy optimization (TRPO) to multi-agent reinforcement learning (MARL) problems. We show that the policy update of TRPO can be transformed into a distribute…
Online Scheduling of a Residential Microgrid via Monte-Carlo Tree Search and a Learned Model
Hang Shuai, Haibo He
The uncertainty of distributed renewable energy brings significant challenges to economic operation of microgrids. Conventional online optimization approaches require a forecast mo…
Nucleus Neural Network: A Data-driven Self-organized Architecture
Jia Liu, Maoguo Gong, Haibo He
Artificial neural networks which are inspired from the learning mechanism of brain have achieved great successes in many problems, especially those with deep layers. In this paper,…
Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning
Hong Huang, Guangyao Shi, Haibo He +2
The graph embedding (GE) methods have been widely applied for dimensionality reduction of hyperspectral imagery (HSI). However, a major challenge of GE is how to choose proper neig…
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach
Hao-Hsuan Chang, Hao Song, Yang Yi +3
Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will fac…
Adaptive Signal Detection and Parameter Estimation in Unknown Colored Gaussian Noise
Bo Tang, Haibo He, Steven Kay
This paper considers the general signal detection and parameter estimation problem in the presence of colored Gaussian noise disturbance. By modeling the disturbance with an autore…
Detection of False Data Injection Attacks in Smart Grid under Colored Gaussian Noise
Bo Tang, Jun Yan, Steven Kay +1
In this paper, we consider the problems of state estimation and false data injection detection in smart grid when the measurements are corrupted by colored Gaussian noise. By model…
A Local Density-Based Approach for Local Outlier Detection
Bo Tang, Haibo He
This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Density-based Outlier Score (RDOS) is…
Probabilistic Human Mobility Model in Indoor Environment
Bo Tang, Chao Jiang, Haibo He +1
Understanding human mobility is important for the development of intelligent mobile service robots as it can provide prior knowledge and predictions of human distribution for robot…
Kernel-based Generative Learning in Distortion Feature Space
Bo Tang, Paul M. Baggenstoss, Haibo He
This paper presents a novel kernel-based generative classifier which is defined in a distortion subspace using polynomial series expansion, named Kernel-Distortion (KD) classifier.…
FSMJ: Feature Selection with Maximum Jensen-Shannon Divergence for Text Categorization
Bo Tang, Haibo He
In this paper, we present a new wrapper feature selection approach based on Jensen-Shannon (JS) divergence, termed feature selection with maximum JS-divergence (FSMJ), for text cat…
EEF: Exponentially Embedded Families with Class-Specific Features for Classification
Bo Tang, Steven Kay, Haibo He +1
In this letter, we present a novel exponentially embedded families (EEF) based classification method, in which the probability density function (PDF) on raw data is estimated from…
Toward Optimal Feature Selection in Naive Bayes for Text Categorization
Bo Tang, Steven Kay, Haibo He
Automated feature selection is important for text categorization to reduce the feature size and to speed up the learning process of classifiers. In this paper, we present a novel a…
Active Dictionary Learning in Sparse Representation Based Classification
Jin Xu, Haibo He, Hong Man
Sparse representation, which uses dictionary atoms to reconstruct input vectors, has been studied intensively in recent years. A proper dictionary is a key for the success of spars…