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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. 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 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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 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 regression-based conditional independence tests Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, (1250-1256), Alaa A, Hu S and van der Schaar M Learning from clinical judgments Proceedings of the 34th International Conference on Machine Learning - Volume 70, (60-69), Chen Y, Hoffman M, Colmenarejo S, Denil M, Lillicrap T, Botvinick M and de Freitas N Learning to learn without gradient descent by gradient descent Proceedings of the 34th International Conference on Machine Learning - Volume 70, (748-756), Chowdhury S and Gopalan A On kernelized multi-armed bandits Proceedings of the 34th International Conference on Machine Learning - Volume 70, (844-853), Cutajar K, Bonilla E, Michiardi P and Filippone M Random feature expansions for Deep Gaussian Processes Proceedings of the 34th International Conference on Machine Learning - Volume 70, (884-893), Daxberger E and Low B Distributed batch Gaussian process optimization Proceedings of the 34th International Conference on Machine Learning - Volume 70, (951-960), Futoma J, Hariharan S and Heller K Learning to detect sepsis with a multitask Gaussian process RNN classifier Proceedings of the 34th International Conference on Machine Learning - Volume 70, (1174-1182), González J, Dai Z, Damianou A and Lawrence N Preferential Bayesian optimization Proceedings of the 34th International Conference on Machine Learning - Volume 70, (1282-1291), Jenatton R, Archambeau C, Gonzalez J and Seeger M Bayesian optimization with tree-structured dependencies Proceedings of the 34th International Conference on Machine Learning - Volume 70, (1655-1664), Kandasamy K, Dasarathy G, Schneider J and Póczos B Multi-fidelity Bayesian optimisation with continuous approximations Proceedings of the 34th International Conference on Machine Learning - Volume 70, (1799-1808), Lyu Y Spherical structured feature maps for kernel approximation Proceedings of the 34th International Conference on Machine Learning - Volume 70, (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 - Volume 70, (3550-3559), Walder C and Bishop A Fast Bayesian intensity estimation for the permanental process Proceedings of the 34th International Conference on Machine Learning - Volume 70, (3579-3588), Wang Z and Jegelka S Max-value entropy search for efficient Bayesian Optimization Proceedings of the 34th International Conference on Machine Learning - Volume 70, (3627-3635), Wei P, Sagarna R, Ke Y, Ong Y and Goh C Source-target similarity modelings for multi-source transfer Gaussian process regression Proceedings of the 34th International Conference on Machine Learning - Volume 70, (3722-3731), Luo C and Sun S Variational mixtures of Gaussian processes for classification Proceedings of the 26th International Joint Conference on Artificial Intelligence, (4603-4609), De G. Matthews A, Van Der Wilk M, Nickson T, Fujii K, Boukouvalas A, León-Villagrá P, Ghahramani Z and Hensman J, Vinogradska J, Bischoff B, Nguyen-Tuong D and Peters J, Andersen M, Vehtari A, Winther O and Hansen L, Al-Shedivat M, Wilson A, Saatchi Y, Hu Z and Xing E, Baydin A, Pearlmutter B, Radul A and Siskind J, Carbajal J, Leito J, Albert C and Rieckermann J, Gonzalez-Navarro P, Moghadamfalahi M, Akcakaya M and Erdogmus D, Ariizumi R, Tesch M, Kato K, Choset H and Matsuno F, Liu S, Maljovec D, Wang B, Bremer P and Pascucci V, Gai M, Mrki N, Rojas-Barahona L, Su P, Ultes S, Vandyke D, Wen T and Young S, Mayfield H, Smith C, Gallagher M and Hockings M, Gauchi J, Bensadoun A, Colas F and Colbach N, Benamara T, Breitkopf P, Lepot I, Sainvitu C and Villon P, Verma M, Thirumalaiselvi A and Rajasankar J, Li T, Zeng C, Zhou W, Xue W, Huang Y, Liu Z, Zhou Q, Xia B, Wang Q, Wang W and Zhu X, Kupcsik A, Deisenroth M, Peters J, Loh A, Vadakkepat P and Neumann G, Cotronei M, Di Salvo R, Holschneider 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International Conference on Computer-Aided Design, (1-8), Xiong X, Filippone M and Vinciarelli A Looking Good With Flickr Faves Proceedings of the 24th ACM international conference on Multimedia, (412-415), Hwang S, Kim S, He Y, Elnikety S and Choi S, Pivarski J, Bennett C and Grossman R Deploying Analytics with the Portable Format for Analytics (PFA) Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (579-588), An B, Chen H, Park N and Subrahmanian V MAP Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (421-430), Lane F, Azad R and Ryan C Principled Evolutionary Algorithm Design and the Kernel Trick Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, (149-150), Safarzadegan Gilan S, Goyal N and Dilkina B Active Learning in Multi-objective Evolutionary Algorithms for Sustainable Building Design Proceedings of the Genetic and Evolutionary Computation 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