Search Results - "Kian Hsiang Low"

Autonomic mobile sensor network with self-coordinated task allocation and execution by Kian Hsiang Low, Leow, W.K., Ang, M.H.

“…This paper describes a distributed layered architecture for resource-constrained multirobot cooperation, which is utilized in autonomic mobile sensor network…”
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Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems by Jie Chen, Kian Hsiang Low, Yujian Yao, Jaillet, Patrick

“…Mobility-on-demand (MoD) systems have recently emerged as a promising paradigm of one-way vehicle sharing for sustainable personal urban mobility in densely…”
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Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception by Ouyang, Ruofei, Low, Bryan Kian Hsiang

“…This paper presents novel Gaussian process decentralized data fusion algorithms exploiting the notion of agent-centric support sets for distributed cooperative…”
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Telesupervised remote surface water quality sensing by Podnar, Gregg, Dolan, John M, Kian Hsiang Low, Elfes, Alberto

“…We present a fleet of autonomous Robot Sensor Boats (RSBs) developed for lake and river fresh water quality assessment and controlled by our Multilevel…”
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Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten by Nguyen, Quoc Phong, Oikawa, Ryutaro, Divakaran, Dinil Mon, Chan, Mun Choon, Low, Bryan Kian Hsiang

“…As the use of machine learning (ML) models is becoming increasingly popular in many real-world applications, there are practical challenges that need to be…”
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Multi-agent ad hoc team partitioning by observing and modeling single-agent performance by Ozgul, Etkin Baris, Liemhetcharat, Somchaya, Kian Hsiang Low

“…Multi-agent research has focused on finding the optimal team for a task. Many approaches assume that the performance of the agents are known a priori. We are…”
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Decision-theoretic coordination and control for active multi-camera surveillance in uncertain, partially observable environments by Natarajan, P., Trong Nghia Hoang, Kian Hsiang Low, Kankanhalli, M.

“…A central problem of surveillance is to monitor multiple targets moving in a large-scale, obstacle-ridden environment with occlusions. This paper presents a…”
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Hierarchical Bayesian Nonparametric Approach to Modeling and Learning the Wisdom of Crowds of Urban Traffic Route Planning Agents by Jiangbo Yu, Kian Hsiang Low, Oran, A., Jaillet, P.

“…Route prediction is important to analyzing and understanding the route patterns and behavior of traffic crowds. Its objective is to predict the most likely or…”
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Pruning during training by network efficacy modeling by Rajpal, Mohit, Zhang, Yehong, Low, Bryan Kian Hsiang

“…Deep neural networks (DNNs) are costly to train. Pruning, an approach to alleviate model complexity by zeroing out or pruning DNN elements, has shown promise…”
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Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression by Yu, Haibin, Nghia, Trong, Hsiang Low, Bryan Kian, Jaillet, Patrick

“…This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose…”
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Continuous-spaced action selection for single- and multi-robot tasks using cooperative extended Kohonen maps by Kian Hsiang Low, Wee Kheng Leow, Ang, M.H.

“…Action selection is a central issue in the design of behavior-based control architectures for autonomous mobile robots. This paper presents an action selection…”
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GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection by Nguyen, Quoc Phong, Lim, Kar Wai, Divakaran, Dinil Mon, Low, Kian Hsiang, Chan, Mun Choon

“…This paper looks into the problem of detecting network anomalies by analyzing NetFlow records. While many previous works have used statistical models and…”
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Recursive reasoning-based training-time adversarial machine learning by Chen, Yizhou, Dai, Zhongxiang, Yu, Haibin, Low, Bryan Kian Hsiang, Ho, Teck-Hua

“…The training process of a machine learning (ML) model may be subject to adversarial attacks from an attacker who attempts to undermine the test performance of…”
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Adaptive Sampling for Multi-Robot Wide-Area Exploration by Kian Hsiang Low, Gordon, G.J., Dolan, J.M., Khosla, P.

“…The exploration problem is a central issue in mobile robotics. A complete coverage is not practical if the environment is large with a few small hotspots, and…”
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Federated Bayesian Optimization via Thompson Sampling by Dai, Zhongxiang, Low, Kian Hsiang, Jaillet, Patrick

“…Bayesian optimization (BO) is a prominent approach to optimizing expensive-to-evaluate black-box functions. The massive computational capability of edge…”
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Probably Approximate Shapley Fairness with Applications in Machine Learning by Zhou, Zijian, Xu, Xinyi, Sim, Rachael Hwee Ling, Foo, Chuan Sheng, Low, Kian Hsiang

“…The Shapley value (SV) is adopted in various scenarios in machine learning (ML), including data valuation, agent valuation, and feature attribution, as it…”
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Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception by Ouyang, Ruofei, Low, Kian Hsiang

“…This paper presents novel Gaussian process decentralized data fusion algorithms exploiting the notion of agent-centric support sets for distributed cooperative…”
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Bayesian Optimization with Binary Auxiliary Information by Zhang, Yehong, Dai, Zhongxiang, Low, Kian Hsiang

“…This paper presents novel mixed-type Bayesian optimization (BO) algorithms to accelerate the optimization of a target objective function by exploiting…”
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Nonmyopic Gaussian Process Optimization with Macro-Actions by Kharkovskii, Dmitrii, Ling, Chun Kai, Low, Kian Hsiang

“…This paper presents a multi-staged approach to nonmyopic adaptive Gaussian process optimization (GPO) for Bayesian optimization (BO) of unknown, highly complex…”
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Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression by Teng, Tong, Chen, Jie, Zhang, Yehong, Low, Kian Hsiang

“…This paper presents a variational Bayesian kernel selection (VBKS) algorithm for sparse Gaussian process regression (SGPR) models. In contrast to existing GP…”
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