{"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/opportunistic-learning-budgeted-cost","title":"Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams","arxiv_id":"1901.00243","date":"2019-01-02","proceeding":"ICLR 2019 5","authors":["Mohammad Kachuee","Orpaz Goldstein","Kimmo Karkkainen","Sajad Darabi","Majid Sarrafzadeh"],"abstract":"In many real-world learning scenarios, features are only acquirable at a cost\nconstrained under a budget. In this paper, we propose a novel approach for\ncost-sensitive feature acquisition at the prediction-time. The suggested method\nacquires features incrementally based on a context-aware feature-value\nfunction. We formulate the problem in the reinforcement learning paradigm, and\nintroduce a reward function based on the utility of each feature. Specifically,\nMC dropout sampling is used to measure expected variations of the model\nuncertainty which is used as a feature-value function. Furthermore, we suggest\nsharing representations between the class predictor and value function\nestimator networks. The suggested approach is completely online and is readily\napplicable to stream learning setups. The solution is evaluated on three\ndifferent datasets including the well-known MNIST dataset as a benchmark as\nwell as two cost-sensitive datasets: Yahoo Learning to Rank and a dataset in\nthe medical domain for diabetes classification. According to the results, the\nproposed method is able to efficiently acquire features and make accurate\npredictions.","url_abs":"http://arxiv.org/abs/1901.00243v2","url_pdf":"http://arxiv.org/pdf/1901.00243v2.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":"opportunistic-learning-budgeted-cost","repo_url":"https://github.com/mkachuee/Opportunistic","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"learning-to-rank","task_name":"Learning-To-Rank"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"}],"methods":[{"method_slug":"dropout","method_name":"Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1901.00243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.00243"}},"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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