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10/20/2020
Federated Bayesian Optimization via Thompson Sampling
Bayesian optimization (BO) is a prominent approach to optimizing expensi...
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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...
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02/22/2020
Nonmyopic Gaussian Process Optimization with Macro-Actions
This paper presents a multi-staged approach to nonmyopic adaptive Gaussi...
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12/05/2019
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression
This paper presents a variational Bayesian kernel selection (VBKS) algor...
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11/16/2019
Inverse Reinforcement Learning with Missing Data
We consider the problem of recovering an expert's reward function with i...
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10/26/2019
Implicit Posterior Variational Inference for Deep Gaussian Processes
A multi-layer deep Gaussian process (DGP) model is a hierarchical compos...
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06/17/2019
Bayesian Optimization with Binary Auxiliary Information
This paper presents novel mixed-type Bayesian optimization (BO) algorith...
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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...
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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...
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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 ...
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11/19/2017
Decentralized High-Dimensional Bayesian Optimization with Factor Graphs
This paper presents a novel decentralized high-dimensional Bayesian opti...
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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...
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11/01/2017
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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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...
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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...
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11/23/2015
Multi-Agent Continuous Transportation with Online Balanced Partitioning
We introduce the concept of continuous transportation task to the contex...
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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...
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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...
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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...
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08/09/2014
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Gaussian processes (GP) are Bayesian non-parametric models that are wide...
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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 ...
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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...
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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...
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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...
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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...
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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...
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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 ...
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01/28/2011
