{"comment":{"url":"https://api.github.com/repos/cornellius-gp/gpytorch/comments/29743835","html_url":"https://github.com/cornellius-gp/gpytorch/commit/3d4d937590fdb4b2a4a18cac7249af30b78ed949#commitcomment-29743835","id":29743835,"node_id":"MDEzOkNvbW1pdENvbW1lbnQyOTc0MzgzNQ==","user":{"login":"jacobrgardner","id":4016393,"node_id":"MDQ6VXNlcjQwMTYzOTM=","avatar_url":"https://avatars0.githubusercontent.com/u/4016393?v=4","gravatar_id":"","url":"https://api.github.com/users/jacobrgardner","html_url":"https://github.com/jacobrgardner","followers_url":"https://api.github.com/users/jacobrgardner/followers","following_url":"https://api.github.com/users/jacobrgardner/following{/other_user}","gists_url":"https://api.github.com/users/jacobrgardner/gists{/gist_id}","starred_url":"https://api.github.com/users/jacobrgardner/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jacobrgardner/subscriptions","organizations_url":"https://api.github.com/users/jacobrgardner/orgs","repos_url":"https://api.github.com/users/jacobrgardner/repos","events_url":"https://api.github.com/users/jacobrgardner/events{/privacy}","received_events_url":"https://api.github.com/users/jacobrgardner/received_events","type":"User","site_admin":false},"position":18,"line":18,"path":"gpytorch/gpytorchopt/gpytorchopt/bayesian_optimization.py","commit_id":"3d4d937590fdb4b2a4a18cac7249af30b78ed949","created_at":"2018-07-18T04:17:03Z","updated_at":"2018-07-18T04:17:03Z","author_association":"MEMBER","body":"I think that sounds like a good plan. As a note, with the exception of a few actual applications, most of my experience running BayesOpt is on the kind of experiments you run for a paper, not really the kind of things you find in a production environment. As a result, I'm pretty okay with deferring to you guys on interface and data handling choices :-). \r\n\r\nThe code here is essentially a port of the code I'd written for the additive structure paper. I was hoping that writing a few simple bayesopt examples would help shine light on what things are still lacking in GPyTorch as a whole for bayesopt functionality."}}
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