[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-cubas-picks-training-data-by-geometry-not-just-size":10,"sections":35},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},4254,"cubas-picks-training-data-by-geometry-not-just-size","CuBAS Picks Training Data by Geometry, Not Just Size","A new sampling framework uses data-manifold curvature to build smaller, more informative training sets for supervised classification.","A research method called CuBAS wants to fix how machine learning models choose their training data.\n\nMost sampling strategies treat a dataset as a flat list and pull examples at random or by uncertainty scores. CuBAS takes a different approach: it models the labeled dataset as a statistical manifold — a geometric surface — and estimates local curvature at each data point using the ratio of second to first-order observed Fisher information, derived from a Potts Markov random field. It then builds a k-nearest-neighbor graph and assigns every node a curvature score. Low-curvature zones are smooth, redundant clusters; high-curvature zones hug decision boundaries, where examples do the most classification work. CuBAS samples from both.\n\nThe payoff, if the benchmarks hold up, is real: the researchers tested CuBAS across more than 60 datasets and reported consistent, statistically significant gains over random sampling and uncertainty-based baselines across a range of budgets and classifier types. That breadth matters because most sampling papers cherry-pick favorable evaluations. The method also scales linearly with the number of k-NN graph edges, so it does not blow up on large datasets.\n\nData-centric AI has been a talking point since Andrew Ng pushed it hard a few years ago, but most production pipelines still lean on brute-force data collection. CuBAS will not change that overnight — it is an arXiv preprint, not a shipping library — but it is a tidy demonstration that geometry-aware selection can squeeze more signal out of fewer labels.","[\"machine learning\",\"data selection\",\"ai research\",\"classification\"]","2026-07-07T04:00:00.000Z","2026-07-07T21:20:01.239Z","2026-07-07T21:20:04.198Z","published",null,[],"ai",[26,27,28,29],"machine learning","data selection","ai research","classification",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.03145",0,{"sections":36},[37,41,46,51,56,61,66,71,76,80,85,89,94,99],{"name":38,"slug":24,"count":39,"latest_published_at":40},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":42,"slug":43,"count":44,"latest_published_at":45},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":47,"slug":48,"count":49,"latest_published_at":50},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":52,"slug":53,"count":54,"latest_published_at":55},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":57,"slug":58,"count":59,"latest_published_at":60},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":62,"slug":63,"count":64,"latest_published_at":65},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":72,"slug":73,"count":74,"latest_published_at":75},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":18},"Dev Tools","dev-tools",59,{"name":81,"slug":82,"count":83,"latest_published_at":84},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":86,"slug":87,"count":83,"latest_published_at":88},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":95,"slug":96,"count":97,"latest_published_at":98},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":100,"slug":101,"count":102,"latest_published_at":103},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]