{"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/scaling-the-indian-buffet-process-via","title":"Scaling the Indian Buffet Process via Submodular Maximization","arxiv_id":"1304.3285","date":"2013-04-11","proceeding":null,"authors":["Colorado Reed","Zoubin Ghahramani"],"abstract":"Inference for latent feature models is inherently difficult as the inference\nspace grows exponentially with the size of the input data and number of latent\nfeatures. In this work, we use Kurihara & Welling (2008)'s\nmaximization-expectation framework to perform approximate MAP inference for\nlinear-Gaussian latent feature models with an Indian Buffet Process (IBP)\nprior. This formulation yields a submodular function of the features that\ncorresponds to a lower bound on the model evidence. By adding a constant to\nthis function, we obtain a nonnegative submodular function that can be\nmaximized via a greedy algorithm that obtains at least a one-third\napproximation to the optimal solution. Our inference method scales linearly\nwith the size of the input data, and we show the efficacy of our method on the\nlargest datasets currently analyzed using an IBP model.","url_abs":"http://arxiv.org/abs/1304.3285v4","url_pdf":"http://arxiv.org/pdf/1304.3285v4.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":"scaling-the-indian-buffet-process-via","repo_url":"https://github.com/cjrd/MEIBP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1304.3285","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}