{"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/pillar-how-to-make-semi-private-learning-more","title":"PILLAR: How to make semi-private learning more effective","arxiv_id":"2306.03962","date":"2023-06-06","proceeding":null,"authors":["Francesco Pinto","Yaxi Hu","Fanny Yang","Amartya Sanyal"],"abstract":"In Semi-Supervised Semi-Private (SP) learning, the learner has access to both public unlabelled and private labelled data. We propose a computationally efficient algorithm that, under mild assumptions on the data, provably achieves significantly lower private labelled sample complexity and can be efficiently run on real-world datasets. For this purpose, we leverage the features extracted by networks pre-trained on public (labelled or unlabelled) data, whose distribution can significantly differ from the one on which SP learning is performed. To validate its empirical effectiveness, we propose a wide variety of experiments under tight privacy constraints (\\(\\epsilon=0.1\\)) and with a focus on low-data regimes. 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