# gaussian processes for machine learning bibtex

Do (updated by Honglak Lee) November 22, 2008 Many of the classical machine learning algorithms that we talked about during the ﬁrst half of this course ﬁt the following pattern: given a training set of i.i.d. Secondly, we will discuss practical matters regarding the role of hyper-parameters in the covariance function, the marginal likelihood and the automatic Occam’s razor. This is achieved by conditioning the distribution on the training data $\mathcal{D}$ yielding the posterior Gaussian Process $f \rvert \mathcal{D} \sim \mathcal{GP}(m_D(\pmb{x}), k_D(\pmb{x},\pmb{x}'))$ for noise-free observations with the posterior mean function $m_D(\pmb{x}) = m(\pmb{x}) + \pmb{\Sigma}(\pmb{X},\pmb{x})^T \pmb{\Sigma}^{-1}(\pmb{\mathrm{f}} - \pmb{\mathrm{m}})$ and the posterior covariance function $k_D(\pmb{x},\pmb{x}')=k(\pmb{x},\pmb{x}') - \pmb{\Sigma}(\pmb{X}, \pmb{x}')$ with $\pmb{\Sigma}(\pmb{X},\pmb{x})$ being a vector of covariances between every training case of $\pmb{X}$ and $\pmb{x}$. The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. The Gaussian Processes Classifier is a classification machine learning algorithm. The book is concerned with supervised learning, that is, the problem of learning input-output mappings from empirical data. The machine learning field calibration method applies Gaussian Process Regression (GPR) and includes two components: (1.) u1(x) ∼ GP(0, k1(x, x ′; θ1)), u2(x) ∼ GP(0, k2(x, x ′; θ2)), are two independent Gaussian processes. 2. It should be noted that a regularization term is not necessary for the log marginal likelihood $L$ because it already contains a complexity penalty term. Also, the tradeoff between data-fit and penalty is performed automatically. Learning and Control using Gaussian Processes Towards bridging machine learning and controls for physical systems Achin Jain? GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Chapter 7 investigates the Gaussian processes from a theoretical point of view. Given a set of observed real-valued points over a space, the Gaussian Process is used to make inference on the values at the remaining points in the space. Huang X, Yang Y and Bao X Grid-based Gaussian Processes Factorization Machine for Recommender Systems Proceedings of the 9th International Conference on Machine Learning and Computing, (92-97) Wu S, Mortveit H and Gupta S A Framework for Validation of Network-based Simulation Models Proceedings of the 2017 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, (197 … I. Williams, Christopher K. I. II. The distribution of a Gaussian process is the joint distribution of all those random variables, and as such, it is a distribution over functions with a … For broader introductions to Gaussian processes, consult [1], [2]. the hyperparameters, and III. The first part, chapters 1 through 5, is devoted to specific topics in the area of Gaussian modeling in supervised learning. The higher degrees of polynomials you choose, the better it will fit the observations. Abstract. 1. The final sections of this chapter focus on other families of kernel machines that are related to Gaussian process prediction, support vector machines, least-squares classification, and vector machines. The advent of kernel machines, such as Support Vector Machines and Gaussian Processes has opened the possibility of flexible models which are practical to work with. Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, (1-4), Li C, Gupta S, Rana S, Nguyen V, Venkatesh S and Shilton A High dimensional Bayesian optimization using dropout Proceedings of the 26th International Joint Conference on Artificial Intelligence, (2096-2102), Xu Z, Kersting K and Ritter L Stochastic online anomaly analysis for streaming time series Proceedings of the 26th International Joint Conference on Artificial Intelligence, (3189-3195), Xiang Q, Zhang J, Nevat I and Zhang P A trust-based mixture of Gaussian processes model for reliable regression in participatory sensing Proceedings of the 26th International Joint Conference on Artificial Intelligence, (3866-3872), Baraldi P, Di Maio F, Al-Dahidi S, Zio E and Mangili F, Portelette L, Roux J, Robin V and Feulvarch E, Ogilvie W, Petoumenos P, Wang Z and Leather H Minimizing the cost of iterative compilation with active learning Proceedings of the 2017 International Symposium on Code Generation and Optimization, (245-256), Albrecht S and Stone P Reasoning about Hypothetical Agent Behaviours and their Parameters Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, (547-555), Xiang Q, Zhang J, Nevat I and Zhang P A Trust-based Mixture of Gaussian Processes Model for Robust Participatory Sensing Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, (1760-1762), Liu X, Khankan A, Alsioufi M, He Z and Ngai E Poster: Cloud-Based Data Fusion in GreenIoT for Smart Cities Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, (216-217), Pagán J, Moya J, Risco-Martín J and Ayala J Advanced migraine prediction simulation system Proceedings of the Summer Simulation Multi-Conference, (1-12), Dutordoir V, Knudde N, van der Herten J, Couckuyt I and Dhaene T Deep gaussian process metamodeling of sequentially sampled non-stationary response surfaces Proceedings of the 2017 Winter Simulation Conference, (1-12), Chen X, Hemmati S and Yang F Stochastic co-kriging for steady-state simulation metamodeling Proceedings of the 2017 Winter Simulation Conference, (1-12), Singh P and Hellander A Surrogate assisted model reduction for stochastic biochemical reaction networks Proceedings of the 2017 Winter Simulation Conference, (1-11), Huang Z, Lam H and Zhao D Sequential experimentation to efficiently test automated vehicles Proceedings of the 2017 Winter Simulation Conference, (1-12), Xie G and Chen X A heteroscedastic T-process simulation metamodeling approach and its application in inventory control and optimization Proceedings of the 2017 Winter Simulation Conference, (1-12), Oates C, Niederer S, Lee A, Briol F and Girolami M Probabilistic models for integration error in the assessment of functional cardiac models Proceedings of the 31st International Conference on Neural Information Processing Systems, (109-117), Liu J and Coull B Robust hypothesis test for nonlinear effect with Gaussian processes Proceedings of the 31st International Conference on Neural Information Processing Systems, (795-803), Ambrogioni L, Hinne M, van Gerven M and Maris E GP CaKe Proceedings of the 31st International Conference on Neural Information Processing Systems, (951-960), Jidling C, Wahlstrom N, Wills A and Schön T Linearly constrained Gaussian processes Proceedings of the 31st International Conference on Neural Information Processing Systems, (1215-1224), Schulam P and Saria S Reliable decision support using counterfactual models Proceedings of the 31st International Conference on Neural Information Processing Systems, (1696-1706), Acerbi L and Ma W Practical Bayesian optimization for model fitting with Bayesian adaptive direct search Proceedings of the 31st International Conference on Neural Information Processing Systems, (1834-1844), Lam R and Willcox K Lookahead Bayesian optimization with inequality constraints Proceedings of the 31st International Conference on Neural Information Processing Systems, (1888-1898), Ciliberto C, Rudi A, Rosasco L and Pontil M Consistent multitask learning with nonlinear output relations Proceedings of the 31st International Conference on Neural Information Processing Systems, (1983-1993), McInerney J An empirical bayes approach to optimizing machine learning algorithms Proceedings of the 31st International Conference on Neural Information Processing Systems, (2709-2718), Bui T, Nguyen C and Turner R Streaming sparse Gaussian process approximations Proceedings of the 31st International Conference on Neural Information Processing Systems, (3301-3309), Vellanki P, Rana S, Gupta S, Rubin D, Sutti A, Dorin T, Height M, Sandars P and Venkatesh S Process-constrained batch Bayesian optimisation Proceedings of the 31st International Conference on Neural Information Processing Systems, (3417-3426), Alaa A and van der Schaar M Bayesian inference of individualized treatment effects using multi-task Gaussian processes Proceedings of the 31st International Conference on Neural Information Processing Systems, (3427-3435), Wu A, Roy N, Keeley S and Pillow J Gaussian process based nonlinear latent structure discovery in multivariate spike train data Proceedings of the 31st International Conference on Neural Information Processing Systems, (3499-3508), Ding Y, Kondor R and Eskreis-Winkler J Multiresolution kernel approximation for Gaussian process regression Proceedings of the 31st International Conference on Neural Information Processing Systems, (3743-3751), Jang P, Loeb A, Davidow M and Wilson A Scalable lévy process priors for spectral kernel learning Proceedings of the 31st International Conference on Neural Information Processing Systems, (3943-3952), Poloczek M, Wang J and Frazier P Multi-information source optimization Proceedings of the 31st International Conference on Neural Information Processing Systems, (4291-4301), Salimbeni H and Deisenroth M Doubly stochastic variational inference for deep Gaussian processes Proceedings of the 31st International Conference on Neural Information Processing Systems, (4591-4602), Remes S, Heinonen M and Kaski S Non-stationary spectral kernels Proceedings of the 31st International Conference on Neural Information Processing Systems, (4645-4654), Sheth R and Khardon R Excess risk bounds for the bayes risk using variational inference in latent Gaussian models Proceedings of the 31st International Conference on Neural Information Processing Systems, (5157-5167), Wu J, Poloczek M, Wilson A and Frazier P Bayesian optimization with gradients Proceedings of the 31st International Conference on Neural Information Processing Systems, (5273-5284), Killian T, Daulton S, Konidaris G and Doshi-Velez F Robust and efficient transfer learning with hidden parameter Markov decision processes Proceedings of the 31st International Conference on Neural Information Processing Systems, (6251-6262), Dong K, Eriksson D, Nickisch H, Bindel D and Wilson A Scalable log determinants for Gaussian process kernel learning Proceedings of the 31st International Conference on Neural Information Processing Systems, (6330-6340), Parra G and Tobar F Spectral mixture kernels for multi-output Gaussian processes Proceedings of the 31st International Conference on Neural Information Processing Systems, (6684-6693), Gallagher N, Ulrich K, Talbot A, Dzirasa K, Carin L and Carlson D Cross-spectral factor analysis Proceedings of the 31st International Conference on Neural Information Processing Systems, (6845-6855), Vu T and Parker D Extracting urban microclimates from electricity bills Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, (4538-4544), You J, Li X, Low M, Lobell D and Ermon S Deep Gaussian process for crop yield prediction based on remote sensing data Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, (4559-4565), Zhang H, Zhou S, Zhang K and Guan J Causal discovery using 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(2256-2264), Palla K, Knowles D and Ghahramani Z A birth-death process for feature allocation Proceedings of the 34th International Conference on Machine Learning - Volume 70, (2751-2759), Pan Y, Yan X, Theodorou E and Boots B Prediction under uncertainty in Sparse Spectrum Gaussian Processes with applications to filtering and control Proceedings of the 34th International Conference on Machine Learning - Volume 70, (2760-2768), Peng H, Zhe S, Zhang X and Qi Y Asynchronous Distributed Variational Gaussian Process for regression Proceedings of the 34th International Conference on Machine Learning - Volume 70, (2788-2797), Umlauft J and Hirche S Learning stable stochastic nonlinear dynamical systems Proceedings of the 34th International Conference on Machine Learning - Volume 70, (3502-3510), Villacampa-Calvo C and Hernandez-Lobato D Scalable multi-class Gaussian process classification using expectation propagation Proceedings of the 34th International Conference on Machine Learning - 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