{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generic-inference-in-latent-gaussian-process","title":"Generic Inference in Latent Gaussian Process Models","arxiv_id":"1609.00577","date":"2016-09-02","proceeding":null,"authors":["Edwin V. Bonilla","Karl Krauth","Amir Dezfouli"],"abstract":"We develop an automated variational method for inference in models with\nGaussian process (GP) priors and general likelihoods. The method supports\nmultiple outputs and multiple latent functions and does not require detailed\nknowledge of the conditional likelihood, only needing its evaluation as a\nblack-box function. Using a mixture of Gaussians as the variational\ndistribution, we show that the evidence lower bound and its gradients can be\nestimated efficiently using samples from univariate Gaussian distributions.\nFurthermore, the method is scalable to large datasets which is achieved by\nusing an augmented prior via the inducing-variable approach underpinning most\nsparse GP approximations, along with parallel computation and stochastic\noptimization. We evaluate our approach quantitatively and qualitatively with\nexperiments on small datasets, medium-scale datasets and large datasets,\nshowing its competitiveness under different likelihood models and sparsity\nlevels. On the large-scale experiments involving prediction of airline delays\nand classification of handwritten digits, we show that our method is on par\nwith the state-of-the-art hard-coded approaches for scalable GP regression and\nclassification.","url_abs":"http://arxiv.org/abs/1609.00577v2","url_pdf":"http://arxiv.org/pdf/1609.00577v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"generic-inference-in-latent-gaussian-process","repo_url":"https://github.com/Karl-Krauth/Sparse-GP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"stochastic-optimization","task_name":"Stochastic Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}