Shuai Tang

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All published works
Action Title Year Authors
+ PDF Chat Improved Differentially Private Regression via Gradient Boosting 2024 Shuai Tang
Sergül Aydöre
Michael Kearns
Saeyoung Rho
Aaron Roth
Yichen Wang
Yu-Xiang Wang
Zhiwei Steven Wu
+ Improved Differentially Private Regression via Gradient Boosting 2023 Shuai Tang
Sergül Aydöre
Michael Kearns
Saeyoung Rho
Aaron Roth
Yichen Wang
Yuxiang Wang
Zhiwei Steven Wu
+ Scalable Membership Inference Attacks via Quantile Regression 2023 Martín Bertrán
Shuai Tang
Michael Kearns
Jamie Morgenstern
Aaron Roth
Zhiwei Steven Wu
+ Membership Inference Attacks on Diffusion Models via Quantile Regression 2023 Shuai Tang
Zhiwei Steven Wu
Sergül Aydöre
Michael Kearns
Aaron Roth
+ MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation 2022 Linhao Luo
Yumeng Li
Buyu Gao
Shuai Tang
Sinan Wang
Jiancheng Li
Tanchao Zhu
Jiancai Liu
Zhao Li
Binqiang Zhao
+ Spectrally Adaptive Common Spatial Patterns 2022 Mahta Mousavi
Eric Lybrand
Shuangquan Feng
Shuai Tang
Rayan Saab
Virginia R. de
+ Private Synthetic Data for Multitask Learning and Marginal Queries 2022 Giuseppe Vietri
Cédric Archambeau
Sergül Aydöre
William Brown
Michael Kearns
Aaron Roth
Ankit Siva
Shuai Tang
Zhiwei Steven Wu
+ Fast Adaptation with Linearized Neural Networks 2021 Wesley J. Maddox
Shuai Tang
Pablo García Moreno
Andrew Gordon Wilson
Andreas Damianou
+ Improving Style Transfer with Calibrated Metrics 2019 Mao-Chuang Yeh
Shuai Tang
Anand Bhattad
Chuhang Zou
David Forsyth
+ Improving Sentence Representations with Consensus Maximisation 2018 Shuai Tang
Virginia R. de
Common Coauthors
Commonly Cited References
Action Title Year Authors # of times referenced
+ Revisiting Differentially Private Regression: Lessons From Learning Theory and their Consequences 2015 Xi Wu
Matt Fredrikson
Wentao Wu
Somesh Jha
Jeffrey F. Naughton
1
+ PDF Chat Deep Learning with Differential Privacy 2016 Martı́n Abadi
Andy Chu
Ian Goodfellow
H. Brendan McMahan
Ilya Mironov
Kunal Talwar
Li Zhang
1
+ Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain 2018 Yu-Xiang Wang
1
+ Differentially Private Empirical Risk Minimization 2009 Kamalika Chaudhuri
Claire Monteleoni
Anand D. Sarwate
1
+ PDF Chat Differentially Private Ordinary Least Squares 2019 Or Sheffet
1
+ Private selection from private candidates 2019 Jingcheng Liu
Kunal Talwar
1
+ PDF Chat Privacy-Preserving Gradient Boosting Decision Trees 2020 Qinbin Li
Zhaomin Wu
Zeyi Wen
Bingsheng He
1
+ PDF Chat The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy 2021 Tommaso Cai
Yichen Wang
Linjun Zhang
1
+ PDF Chat The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection 2022 Shubhankar Mohapatra
Sajin Sasy
Xi He
Gautam Kamath
Om Thakkar
1
+ PDF Chat Gaussian Differential Privacy 2022 Jinshuo Dong
Aaron Roth
Weijie Su
1
+ PDF Chat Differentially Private Simple Linear Regression 2022 Daniel Alabi
Audra McMillan
Jayshree Sarathy
Adam Smith
Salil Vadhan
1
+ DP-XGBoost: Private Machine Learning at Scale 2021 Nicolas Grislain
Joan Gonzalvez
1
+ Easy Differentially Private Linear Regression 2022 Kareem Amin
Matthew Joseph
Mónica Ribero
Sergei Vassilvitskii
1
+ Why do tree-based models still outperform deep learning on tabular data? 2022 Léo Grinsztajn
Edouard Oyallon
Gaël Varoquaux
1
+ Practical Differentially Private Hyperparameter Tuning with Subsampling 2023 Antti Koskela
Tejas Kulkarni
1