{"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/efficient-loss-based-decoding-on-graphs-for","title":"Efficient Loss-Based Decoding on Graphs For Extreme Classification","arxiv_id":"1803.03319","date":"2018-03-08","proceeding":"NeurIPS 2018 12","authors":["Itay Evron","Edward Moroshko","Koby Crammer"],"abstract":"In extreme classification problems, learning algorithms are required to map\ninstances to labels from an extremely large label set. We build on a recent\nextreme classification framework with logarithmic time and space, and on a\ngeneral approach for error correcting output coding (ECOC) with loss-based\ndecoding, and introduce a flexible and efficient approach accompanied by\ntheoretical bounds. Our framework employs output codes induced by graphs, for\nwhich we show how to perform efficient loss-based decoding to potentially\nimprove accuracy. In addition, our framework offers a tradeoff between\naccuracy, model size and prediction time. We show how to find the sweet spot of\nthis tradeoff using only the training data. Our experimental study demonstrates\nthe validity of our assumptions and claims, and shows that our method is\ncompetitive with state-of-the-art algorithms.","url_abs":"http://arxiv.org/abs/1803.03319v2","url_pdf":"http://arxiv.org/pdf/1803.03319v2.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":"efficient-loss-based-decoding-on-graphs-for","repo_url":"https://github.com/ievron/wltls","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"ltls","method_name":"LTLS"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.03319","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.03319"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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