
Federated Bayesian Optimization via Thompson Sampling
Bayesian optimization (BO) is a prominent approach to optimizing expensi...
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R2B2: Recursive ReasoningBased Bayesian Optimization for NoRegret Learning in Games
This paper presents a recursive reasoning formalism of Bayesian optimiza...
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Nonmyopic Gaussian Process Optimization with MacroActions
This paper presents a multistaged approach to nonmyopic adaptive Gaussi...
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Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression
This paper presents a variational Bayesian kernel selection (VBKS) algor...
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Inverse Reinforcement Learning with Missing Data
We consider the problem of recovering an expert's reward function with i...
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Implicit Posterior Variational Inference for Deep Gaussian Processes
A multilayer deep Gaussian process (DGP) model is a hierarchical compos...
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Bayesian Optimization with Binary Auxiliary Information
This paper presents novel mixedtype Bayesian optimization (BO) algorith...
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GEE: A Gradientbased Explainable Variational Autoencoder for Network Anomaly Detection
This paper looks into the problem of detecting network anomalies by anal...
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Towards Robust ResNet: A Small Step but A Giant Leap
This paper presents a simple yet principled approach to boosting the rob...
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Collective Online Learning via Decentralized Gaussian Processes in Massive MultiAgent Systems
Distributed machine learning (ML) is a modern computation paradigm that ...
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Decentralized HighDimensional Bayesian Optimization with Factor Graphs
This paper presents a novel decentralized highdimensional Bayesian opti...
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Gaussian Process Decentralized Data Fusion Meets Transfer Learning in LargeScale Distributed Cooperative Perception
This paper presents novel Gaussian process decentralized data fusion alg...
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Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models
This paper presents a novel variational inference framework for deriving...
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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...
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DrMAD: Distilling ReverseMode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks
The performance of deep neural networks is wellknown to be sensitive to...
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MultiAgent Continuous Transportation with Online Balanced Partitioning
We introduce the concept of continuous transportation task to the contex...
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NearOptimal Active Learning of MultiOutput Gaussian Processes
This paper addresses the problem of active learning of a multioutput Ga...
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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...
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Parallel Gaussian Process Regression for Big Data: LowRank Representation Meets Markov Approximation
The expressive power of a Gaussian process (GP) model comes at a cost of...
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Parallel Gaussian Process Regression with LowRank Covariance Matrix Approximations
Gaussian processes (GP) are Bayesian nonparametric models that are wide...
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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 ...
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GPLocalize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model
Central to robot exploration and mapping is the task of persistent local...
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InformationTheoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing
Recent research in robot exploration and mapping has focused on sampling...
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Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with SelfInterested Agents
A key challenge in noncooperative multiagent systems is that of develo...
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A General Framework for Interacting BayesOptimally with SelfInterested Agents using Arbitrary Parametric Model and Model Prior
Recent advances in Bayesian reinforcement learning (BRL) have shown that...
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MultiRobot 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...
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DecisionTheoretic Coordination and Control for Active MultiCamera Surveillance in Uncertain, Partially Observable Environments
A central problem of surveillance is to monitor multiple targets moving ...
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Active Markov InformationTheoretic Path Planning for Robotic Environmental Sensing
Recent research in multirobot exploration and mapping has focused on sa...
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