# gaussian processes for machine learning bibtex

The method infers latent state representations from observations using neural networks and models the system dynamics in the learned latent space with Gaussian processes. In this short tutorial we present the basic idea on how Gaussian Process models can be used to formulate a … 2. take the first partial derivatives of $L$ w.r.t. The world of Gaussian processes will remain exciting for the foreseeable as research is being done to bring their probabilistic benefits to problems currently dominated by deep learning — sparse and minibatch Gaussian processes increase their scalability to large datasets while deep and convolutional Gaussian processes put high-dimensional and image data within reach. The problem is approached in terms of different methodologies, Bayesian principles, cross-validation, and the leave-one-out estimator. For broader introductions to Gaussian processes, consult [1], [2]. Covariance Function Gaussian Process Marginal Likelihood Posterior Variance Joint Gaussian Distribution These keywords were added by machine and not by the authors. A function vector $\pmb{\mathrm{f}} = [f(\pmb{x}_1), \dots, f(\pmb{x}_n)]^T$ can be drawn from the Gaussian distribution $\pmb{\mathrm{f}} \sim \mathcal{N}\left(\pmb{\mu}, \pmb{\Sigma} \right)$ Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. CE Rasmussen, H Nickisch. In probability theory and statistics, a Gaussian process is a stochastic process, such that every finite collection of those random variables has a multivariate normal distribution, i.e. Chapter 1 provides an introduction to Bayesian modeling. The book is an excellent and comprehensive monograph on the topic of Gaussian approaches in machine learning. Specifically, we apply the models on the monthly M3 time series competition data (around a thousand time series). Book Abstract: Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. 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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 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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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