{"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-data-subset-selection-to-generalize-1","title":"Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks","arxiv_id":"2409.12255","date":"2024-09-18","proceeding":"NeurIPS 2023 11","authors":["Eeshaan Jain","Tushar Nandy","Gaurav Aggarwal","Ashish Tendulkar","Rishabh Iyer","Abir De"],"abstract":"Existing subset selection methods for efficient learning predominantly employ discrete combinatorial and model-specific approaches which lack generalizability. For an unseen architecture, one cannot use the subset chosen for a different model. To tackle this problem, we propose $\\texttt{SubSelNet}$, a trainable subset selection framework, that generalizes across architectures. Here, we first introduce an attention-based neural gadget that leverages the graph structure of architectures and acts as a surrogate to trained deep neural networks for quick model prediction. Then, we use these predictions to build subset samplers. This naturally provides us two variants of $\\texttt{SubSelNet}$. The first variant is transductive (called as Transductive-$\\texttt{SubSelNet}$) which computes the subset separately for each model by solving a small optimization problem. Such an optimization is still super fast, thanks to the replacement of explicit model training by the model approximator. The second variant is inductive (called as Inductive-$\\texttt{SubSelNet}$) which computes the subset using a trained subset selector, without any optimization. Our experiments show that our model outperforms several methods across several real datasets","url_abs":"https://arxiv.org/abs/2409.12255v1","url_pdf":"https://arxiv.org/pdf/2409.12255v1.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-data-subset-selection-to-generalize-1","repo_url":"https://github.com/structlearning/subselnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2409.12255","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.12255"}},"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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