Kian Hsiang Low

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Patrick Jaillet
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John M. Dolan
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Jingfeng Zhang
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Trong Nghia Hoang
21 publications

research

∙ 10/20/2020

Federated Bayesian Optimization via Thompson Sampling

Bayesian optimization (BO) is a prominent approach to optimizing expensi...

17 Zhongxiang Dai, et al. ∙

research

∙ 06/30/2020

R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games

This paper presents a recursive reasoning formalism of Bayesian optimiza...

11 Zhongxiang Dai, et al. ∙

research

∙ 02/22/2020

Nonmyopic Gaussian Process Optimization with Macro-Actions

This paper presents a multi-staged approach to nonmyopic adaptive Gaussi...

0 Dmitrii Kharkovskii, et al. ∙

research

∙ 12/05/2019

Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression

This paper presents a variational Bayesian kernel selection (VBKS) algor...

28 Tong Teng, et al. ∙

research

∙ 11/16/2019

Inverse Reinforcement Learning with Missing Data

We consider the problem of recovering an expert's reward function with i...

26 Tien Mai, et al. ∙

research

∙ 10/26/2019

Implicit Posterior Variational Inference for Deep Gaussian Processes

A multi-layer deep Gaussian process (DGP) model is a hierarchical compos...

16 Haibin Yu, et al. ∙

research

∙ 06/17/2019

Bayesian Optimization with Binary Auxiliary Information

This paper presents novel mixed-type Bayesian optimization (BO) algorith...

7 Yehong Zhang, et al. ∙

research

∙ 03/15/2019

GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection

This paper looks into the problem of detecting network anomalies by anal...

0 Quoc Phong Nguyen, et al. ∙

research

∙ 02/28/2019

Towards Robust ResNet: A Small Step but A Giant Leap

This paper presents a simple yet principled approach to boosting the rob...

1 Jingfeng Zhang, et al. ∙

research

∙ 05/23/2018

Collective Online Learning via Decentralized Gaussian Processes in Massive Multi-Agent Systems

Distributed machine learning (ML) is a modern computation paradigm that ...

0 Trong Nghia Hoang, et al. ∙

research

∙ 11/19/2017

Decentralized High-Dimensional Bayesian Optimization with Factor Graphs

This paper presents a novel decentralized high-dimensional Bayesian opti...

0 Trong Nghia Hoang, et al. ∙

research

∙ 11/16/2017

Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception

This paper presents novel Gaussian process decentralized data fusion alg...

0 Ruofei Ouyang, et al. ∙

research

∙ 11/01/2017

Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models

This paper presents a novel variational inference framework for deriving...

0 Haibin Yu, et al. ∙

research

∙ 11/18/2016

A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression

While much research effort has been dedicated to scaling up sparse Gauss...

0 Quang Minh Hoang, et al. ∙

research

∙ 01/05/2016

DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks

The performance of deep neural networks is well-known to be sensitive to...

0 Jie Fu, et al. ∙

research

∙ 11/23/2015

Multi-Agent Continuous Transportation with Online Balanced Partitioning

We introduce the concept of continuous transportation task to the contex...

0 Chao Wang, et al. ∙

research

∙ 11/21/2015

Near-Optimal Active Learning of Multi-Output Gaussian Processes

This paper addresses the problem of active learning of a multi-output Ga...

0 Yehong Zhang, et al. ∙

research

∙ 11/21/2015

Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond

This paper presents a novel nonmyopic adaptive Gaussian process planning...

0 Chun Kai Ling, et al. ∙

research

∙ 11/17/2014

Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation

The expressive power of a Gaussian process (GP) model comes at a cost of...

0 Kian Hsiang Low, et al. ∙

research

∙ 08/09/2014

Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations

Gaussian processes (GP) are Bayesian non-parametric models that are wide...

0 Jie Chen, et al. ∙

research

∙ 08/09/2014

Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena

The problem of modeling and predicting spatiotemporal traffic phenomena ...

0 Jie Chen, et al. ∙

research

∙ 04/21/2014

GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model

Central to robot exploration and mapping is the task of persistent local...

0 Nuo Xu, et al. ∙

research

∙ 05/27/2013

Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing

Recent research in robot exploration and mapping has focused on sampling...

0 Kian Hsiang Low, et al. ∙

research

∙ 04/18/2013

Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents

A key challenge in non-cooperative multi-agent systems is that of develo...

0 Trong Nghia Hoang, et al. ∙

research

∙ 04/07/2013

A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior

Recent advances in Bayesian reinforcement learning (BRL) have shown that...

0 Trong Nghia Hoang, et al. ∙

research

∙ 02/04/2013

Multi-Robot Informative Path Planning for Active Sensing of Environmental Phenomena: A Tale of Two Algorithms

A key problem of robotic environmental sensing and monitoring is that of...

0 Nannan Cao, et al. ∙

research

∙ 09/19/2012

Decision-Theoretic Coordination and Control for Active Multi-Camera Surveillance in Uncertain, Partially Observable Environments

A central problem of surveillance is to monitor multiple targets moving ...

0 Prabhu Natarajan, et al. ∙

research

∙ 01/28/2011

Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing

Recent research in multi-robot exploration and mapping has focused on sa...

0 Kian Hsiang Low, et al. ∙

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