Privately Answering Classification Queries in the Agnostic PAC Model
暂无分享,去创建一个
[1] Shai Ben-David,et al. Understanding Machine Learning: From Theory to Algorithms , 2014 .
[2] Martín Abadi,et al. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data , 2016, ICLR.
[3] Adam D. Smith,et al. Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso , 2013, COLT.
[4] Kobbi Nissim,et al. Differentially Private Release and Learning of Threshold Functions , 2015, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science.
[5] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.
[6] Noga Alon,et al. Limits of Private Learning with Access to Public Data , 2019, NeurIPS.
[7] Amos Beimel,et al. Private Learning and Sanitization: Pure vs. Approximate Differential Privacy , 2013, APPROX-RANDOM.
[8] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2016, J. Priv. Confidentiality.
[9] Moni Naor,et al. Our Data, Ourselves: Privacy Via Distributed Noise Generation , 2006, EUROCRYPT.
[10] Sofya Raskhodnikova,et al. Smooth sensitivity and sampling in private data analysis , 2007, STOC '07.
[11] Ninghui Li,et al. On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy , 2011, ASIACCS '12.
[12] Noga Alon,et al. Private PAC learning implies finite Littlestone dimension , 2018, STOC.
[13] Sofya Raskhodnikova,et al. What Can We Learn Privately? , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.
[14] Kunal Talwar,et al. Mechanism Design via Differential Privacy , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).
[15] Shai Ben-David,et al. Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning , 2008, COLT.
[16] Vitaly Feldman,et al. Privacy-preserving Prediction , 2018, COLT.
[17] Mikhail Belkin,et al. Learning privately from multiparty data , 2016, ICML.
[18] Vitaly Feldman,et al. PAC learning with stable and private predictions , 2019, COLT 2020.
[19] Norbert Sauer,et al. On the Density of Families of Sets , 1972, J. Comb. Theory A.
[20] Aaron Roth,et al. The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..
[21] Raef Bassily,et al. Model-Agnostic Private Learning , 2018, NeurIPS.
[22] Úlfar Erlingsson,et al. Scalable Private Learning with PATE , 2018, ICLR.
[23] Amos Beimel,et al. Learning Privately with Labeled and Unlabeled Examples , 2015, SODA.