暂无分享,去创建一个
[1] Joel Nothman,et al. SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.
[2] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.
[3] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2016, J. Priv. Confidentiality.
[4] Zahidul Islam,et al. Decision Tree Classification with Differential Privacy: A Survey , 2016 .
[5] Kunal Talwar,et al. Private selection from private candidates , 2018, STOC.
[6] Vitaly Shmatikov,et al. Exploiting Unintended Feature Leakage in Collaborative Learning , 2018, 2019 IEEE Symposium on Security and Privacy (SP).
[7] Pramod Viswanath,et al. The Composition Theorem for Differential Privacy , 2013, IEEE Transactions on Information Theory.
[8] K. Jarrod Millman,et al. Array programming with NumPy , 2020, Nat..
[9] Pol Mac Aonghusa,et al. Diffprivlib: The IBM Differential Privacy Library , 2019, ArXiv.
[10] Anand D. Sarwate,et al. Differentially Private Empirical Risk Minimization , 2009, J. Mach. Learn. Res..
[11] Vitaly Shmatikov,et al. Differential Privacy Has Disparate Impact on Model Accuracy , 2019, NeurIPS.
[12] Cynthia Rudin,et al. In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction , 2020, ArXiv.
[13] Úlfar Erlingsson,et al. The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets , 2018, ArXiv.
[14] Johannes Gehrke,et al. Intelligible models for classification and regression , 2012, KDD.
[15] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[16] Nilotpal Chakravarti,et al. Isotonic Median Regression: A Linear Programming Approach , 1989, Math. Oper. Res..
[17] Aaron Roth,et al. The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..
[18] Bingsheng He,et al. Privacy-Preserving Gradient Boosting Decision Trees , 2019, AAAI.
[19] R. Tibshirani,et al. Generalized Additive Models , 1986 .
[20] C. vanEeden. Testing and estimating ordered parameters of probability distribution , 1958 .
[21] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[22] Cynthia Rudin,et al. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.
[23] Jinshuo Dong,et al. Deep Learning with Gaussian Differential Privacy , 2020, Harvard data science review.
[24] Rebecca N. Wright,et al. A Practical Differentially Private Random Decision Tree Classifier , 2009, 2009 IEEE International Conference on Data Mining Workshops.
[25] Or Sheffet. Private Approximations of the 2nd-Moment Matrix Using Existing Techniques in Linear Regression , 2015, ArXiv.
[26] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[27] Aaron Roth,et al. Gaussian differential privacy , 2019, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[28] Gilles Louppe,et al. Independent consultant , 2013 .
[29] Pierre Geurts,et al. Extremely randomized trees , 2006, Machine Learning.
[30] Md Zahidul Islam,et al. A Differentially Private Decision Forest , 2015, AusDM.
[31] Johannes Gehrke,et al. Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission , 2015, KDD.
[32] Tie-Yan Liu,et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.
[33] Maria-Florina Balcan,et al. Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning , 2020, ArXiv.
[34] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[35] Johannes Gehrke,et al. Accurate intelligible models with pairwise interactions , 2013, KDD.
[36] Anand D. Sarwate,et al. Symmetric matrix perturbation for differentially-private principal component analysis , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[37] Rich Caruana,et al. InterpretML: A Unified Framework for Machine Learning Interpretability , 2019, ArXiv.
[38] Varun Gupta,et al. On the Compatibility of Privacy and Fairness , 2019, UMAP.
[39] Rich Caruana,et al. How Interpretable and Trustworthy are GAMs? , 2020, KDD.
[40] Roman Garnett,et al. Differentially Private Bayesian Optimization , 2015, ICML.
[41] Leo Breiman,et al. Random Forests , 2001, Machine Learning.