{"data":{"slug":"ziyuw-rembo","name":"rembo","tagline":"Bayesian optimization in high-dimensions via random embedding.","github_url":"https://github.com/ziyuw/rembo","owner":"ziyuw","repo":"rembo","owner_avatar_url":"https://avatars.githubusercontent.com/u/759511?v=4","primary_language":"Matlab","stars":117,"forks":25,"topics":[],"archived":false,"github_pushed_at":"2013-08-04T20:55:23+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/ziyuw-rembo","markdown_url":"https://www.graphcanon.com/tools/ziyuw-rembo.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/ziyuw-rembo","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=ziyuw-rembo","description":"Bayesian optimization in high-dimensions via random embedding.","homepage_url":null,"license":null,"open_issues":3,"watchers":9,"ai_summary":"A Matlab package for conducting Bayesian optimization through random embedding for high-dimensional spaces, including examples and data files for testing the functionality.","readme_excerpt":"### INSTALL\nThe package is written in Matlab. Thus to install, just run startup.m. This \npackage could make use of the function \"fminsearch\" from Matlab. \nThis, however, is not necessary.\n\nTo run the lpsolve example from the paper, please download the data files from:\nhttp://www.cs.ubc.ca/~ziyuw/AC_blackbox_eval.tar.\nAnd untar the data files to the folder /demos/lpsolve/.","github_created_at":"2013-08-04T20:52:59+00:00","created_at":"2026-07-11T23:36:27.940225+00:00","updated_at":"2026-08-04T12:00:49.358068+00:00","categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"bayesian-optimization","name":"bayesian-optimization"},{"slug":"high-dimensional-space","name":"high-dimensional space"},{"slug":"random-embedding","name":"random embedding"}],"trust":{"provenance":{"is_fork":false,"github_id":11884883,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-04T12:00:48.408Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":4747,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:36:31.021Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-04T12:00:48.881Z"},"languages":{"value":["matlab"],"source":"github.language","observed_at":"2026-08-04T12:00:48.881Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When working with high-dimensional data spaces that require efficient exploration and optimization, making it ideal for problems exceeding typical dimensions","If you are already invested in the Matlab environment and seek to leverage existing functions like 'fminsearch' for optimization tasks"],"when_not_to_use":["For low-dimensional spaces where full Bayesian optimization methods would be more efficient and less complex than random embedding","In scenarios requiring open-source or licensed software when Rembo's license status is unknown, potentially limiting its use in certain projects"],"source":"enrich:decision_facts","observed_at":"2026-07-16T23:05:29.206Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Rembo is a Matlab-based tool for high-dimensional Bayesian optimization using random embedding."}]}}