{"action":"created","issue":{"url":"https://api.github.com/repos/SheffieldML/GPy/issues/457","repository_url":"https://api.github.com/repos/SheffieldML/GPy","labels_url":"https://api.github.com/repos/SheffieldML/GPy/issues/457/labels{/name}","comments_url":"https://api.github.com/repos/SheffieldML/GPy/issues/457/comments","events_url":"https://api.github.com/repos/SheffieldML/GPy/issues/457/events","html_url":"https://github.com/SheffieldML/GPy/issues/457","id":188578071,"number":457,"title":"Using Bayesian optimization to fit the parameters of a GP","user":{"login":"achapchap","id":13765435,"avatar_url":"https://avatars.githubusercontent.com/u/13765435?v=3","gravatar_id":"","url":"https://api.github.com/users/achapchap","html_url":"https://github.com/achapchap","followers_url":"https://api.github.com/users/achapchap/followers","following_url":"https://api.github.com/users/achapchap/following{/other_user}","gists_url":"https://api.github.com/users/achapchap/gists{/gist_id}","starred_url":"https://api.github.com/users/achapchap/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/achapchap/subscriptions","organizations_url":"https://api.github.com/users/achapchap/orgs","repos_url":"https://api.github.com/users/achapchap/repos","events_url":"https://api.github.com/users/achapchap/events{/privacy}","received_events_url":"https://api.github.com/users/achapchap/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2016-11-10T18:04:15Z","updated_at":"2016-11-10T19:09:03Z","closed_at":null,"body":"Hi,\r\n \r\nI wonder whether it would be possible to use a GPy in order  to fit a GP (quad GP  ?) to the negative log likehood of another GP (x = hyper parameters and y =nlml) and then try to find the hyper parameters by doing BO. \r\n\r\nThe closest approach I have found was Bayesian quadrature, but if I understood it correctly it tries to model the whole posterior rather than nlml . In addition in this case, should one try to minimize the more conventional acquistions (say EI and UCB) or perhaps try to minimize uncertainty instead  (say the variance of the quad GP)  ? \r\n\r\nThanks very much, \r\n\r\n"},"comment":{"url":"https://api.github.com/repos/SheffieldML/GPy/issues/comments/259778875","html_url":"https://github.com/SheffieldML/GPy/issues/457#issuecomment-259778875","issue_url":"https://api.github.com/repos/SheffieldML/GPy/issues/457","id":259778875,"user":{"login":"alansaul","id":1190833,"avatar_url":"https://avatars.githubusercontent.com/u/1190833?v=3","gravatar_id":"","url":"https://api.github.com/users/alansaul","html_url":"https://github.com/alansaul","followers_url":"https://api.github.com/users/alansaul/followers","following_url":"https://api.github.com/users/alansaul/following{/other_user}","gists_url":"https://api.github.com/users/alansaul/gists{/gist_id}","starred_url":"https://api.github.com/users/alansaul/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/alansaul/subscriptions","organizations_url":"https://api.github.com/users/alansaul/orgs","repos_url":"https://api.github.com/users/alansaul/repos","events_url":"https://api.github.com/users/alansaul/events{/privacy}","received_events_url":"https://api.github.com/users/alansaul/received_events","type":"User","site_admin":false},"created_at":"2016-11-10T19:09:02Z","updated_at":"2016-11-10T19:09:02Z","body":"From what I understand, you essentially want to attempt global optimisation\nof the hyper-parameters using bayesian optimisation (BO).\n\nThis would certainly be possible, I had considered doing it before. There\nis a really great BO package that is built on top of GPy called GPyOpt.\nEssentially you want to just use a GPy model (the nll) as your function.\n\nThere is an example notebook here:\n\nhttps://github.com/SheffieldML/GPyOpt/blob/master/manual/GPyOpt_GPy.ipynb\n\nCheers,\n\nAlan\n\nOn 10 November 2016 at 18:04, achapchap <notifications@github.com> wrote:\n\n> Hi,\n>\n> I wonder whether it would be possible to use a GPy in order to fit a GP\n> (quad GP ?) to the negative log likehood of another GP (x = hyper\n> parameters and y =nlml) and then try to find the hyper parameters by doing\n> BO.\n>\n> The closest approach I have found was Bayesian quadrature, but if I\n> understood it correctly it tries to model the whole posterior rather than\n> nlml . In addition in this case, should one try to minimize the more\n> conventional acquistions (say EI and UCB) or perhaps try to minimize\n> uncertainty instead (say the variance of the quad GP) ?\n>\n> Thanks very much,\n>\n> —\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/SheffieldML/GPy/issues/457>, or mute the thread\n> <https://github.com/notifications/unsubscribe-auth/ABIrsUXx3pmevY_nwRVBsZJT8EzeAjXZks5q81yfgaJpZM4Ku9iW>\n> .\n>\n"}}
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