@inproceedings{wang2019differentially1,
  title={Differentially private empirical risk minimization with non-convex loss functions},
  author={Wang, Di and Chen, Changyou and Xu, Jinhui},
  booktitle={International Conference on Machine Learning},
  pages={6526--6535},
  year={2019},
  organization={PMLR}
}
@article{zhu2024truthful,
  title={Truthful High Dimensional Sparse Linear Regression},
  author={Zhu, Liyang and Manseur, Amina and Ding, Meng and Liu, Jinyan and Xu, Jinhui and Wang, Di},
  journal={arXiv preprint arXiv:2410.13046},
  year={2024}
}
@inproceedings{zhang2025improved,
  title={Improved rates of differentially private nonconvex-strongly-concave minimax optimization},
  author={Zhang, Ruijia and Lei, Mingxi and Ding, Meng and Xiang, Zihang and Xu, Jinhui and Wang, Di},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={21},
  pages={22524--22532},
  year={2025}
}
@article{wang2024gradient,
  title={Gradient complexity and non-stationary views of differentially private empirical risk minimization},
  author={Wang, Di and Xu, Jinhui},
  journal={Theoretical Computer Science},
  volume={982},
  pages={114259},
  year={2024},
  publisher={Elsevier}
}
@article{su2024faster,
  title={Faster rates of differentially private stochastic convex optimization},
  author={Su, Jinyan and Hu, Lijie and Wang, Di},
  journal={Journal of Machine Learning Research},
  volume={25},
  number={114},
  pages={1--41},
  year={2024}
}
@incollection{wang2023finite,
  title={Finite sample guarantees of differentially private expectation maximization algorithm},
  author={Wang, Di and Ding, Jiahao and Hu, Lijie and Xie, Zejun and Pan, Miao and Xu, Jinhui},
  booktitle={ECAI 2023},
  pages={2435--2442},
  year={2023},
  publisher={IOS Press}
}
@article{tao2025second,
  title={Second-Order Convergence in Private Stochastic Non-Convex Optimization},
  author={Tao, Youming and Zhang, Zuyuan and Yu, Dongxiao and Cheng, Xiuzhen and Dressler, Falko and Wang, Di},
  journal={arXiv preprint arXiv:2505.15647},
  year={2025}
}
@inproceedings{su2023differentially,
  title={Differentially private stochastic convex optimization in (non)-Euclidean space revisited},
  author={Su, Jinyan and Zhao, Changhong and Wang, Di},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={2026--2035},
  year={2023},
  organization={PMLR}
}
@article{ding2024understanding,
  title={Understanding forgetting in continual learning with linear regression},
  author={Ding, Meng and Ji, Kaiyi and Wang, Di and Xu, Jinhui},
  journal={arXiv preprint arXiv:2405.17583},
  year={2024}
}
@article{wang2023generalized,
  title={Generalized linear models in non-interactive local differential privacy with public data},
  author={Wang, Di and Hu, Lijie and Zhang, Huanyu and Gaboardi, Marco and Xu, Jinhui},
  journal={Journal of Machine Learning Research},
  volume={24},
  number={132},
  pages={1--57},
  year={2023}
}
@inproceedings{kalai2009isotron,
  title={The Isotron Algorithm: High-Dimensional Isotonic Regression.},
  author={Kalai, Adam Tauman and Sastry, Ravi},
  booktitle={COLT},
  year={2009}
}

@article{kakade2011efficient,
  title={Efficient learning of generalized linear and single index models with isotonic regression},
  author={Kakade, Sham M and Kanade, Varun and Shamir, Ohad and Kalai, Adam},
  journal={Advances in Neural Information Processing Systems},
  volume={24},
  year={2011}
}
@inproceedings{liu2023label,
  title={Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency},
  author={Liu, Xiyang and Jain, Prateek and Kong, Weihao and Oh, Sewoong and Suggala, Arun},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}

@inproceedings{feldman2022hiding,
  title={Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling},
  author={Feldman, Vitaly and McMillan, Audra and Talwar, Kunal},
  booktitle={2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS)},
  pages={954--964},
  year={2022},
  organization={IEEE}
}
@article{liu2022dp,
  title={DP-PCA: Statistically Optimal and Differentially Private PCA},
  author={Liu, Xiyang and Kong, Weihao and Jain, Prateek and Oh, Sewoong},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={29929--29943},
  year={2022}
}
@inproceedings{zou2021benign,
  title={Benign overfitting of constant-stepsize sgd for linear regression},
  author={Zou, Difan and Wu, Jingfeng and Braverman, Vladimir and Gu, Quanquan and Kakade, Sham},
  booktitle={Conference on Learning Theory},
  pages={4633--4635},
  year={2021},
  organization={PMLR}
}

@inproceedings{dwork2006calibrating,
  title={Calibrating noise to sensitivity in private data analysis},
  author={Dwork, Cynthia and McSherry, Frank and Nissim, Kobbi and Smith, Adam},
  booktitle={Theory of Cryptography: Third Theory of Cryptography Conference, TCC 2006, New York, NY, USA, March 4-7, 2006. Proceedings 3},
  pages={265--284},
  year={2006},
  organization={Springer}
}

@inproceedings{bun2016concentrated,
  title={Concentrated differential privacy: Simplifications, extensions, and lower bounds},
  author={Bun, Mark and Steinke, Thomas},
  booktitle={Theory of Cryptography Conference},
  pages={635--658},
  year={2016},
  organization={Springer}
}

@article{varshney2022nearly,
  title={(Nearly) Optimal Private Linear Regression via Adaptive Clipping},
  author={Varshney, Prateek and Thakurta, Abhradeep and Jain, Prateek},
  journal={arXiv preprint arXiv:2207.04686},
  year={2022}
}

@article{wu2023finite,
  title={Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron},
  author={Wu, Jingfeng and Zou, Difan and Chen, Zixiang and Braverman, Vladimir and Gu, Quanquan and Kakade, Sham M},
  year={2023}
}

@article{jain2018parallelizing,
  title={Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification},
  author={Jain, Prateek and Kakade, Sham and Kidambi, Rahul and Netrapalli, Praneeth and Sidford, Aaron},
  journal={Journal of machine learning research},
  volume={18},
  year={2018}
}

@article{shen2023differentially,
  title={Differentially Private Non-convex Learning for Multi-layer Neural Networks},
  author={Shen, Hanpu and Wang, Cheng-Long and Xiang, Zihang and Ying, Yiming and Wang, Di},
  journal={arXiv preprint arXiv:2310.08425},
  year={2023}
}

@article{chaudhuri2011differentially,
  title={Differentially private empirical risk minimization.},
  author={Chaudhuri, Kamalika and Monteleoni, Claire and Sarwate, Anand D},
  journal={Journal of Machine Learning Research},
  volume={12},
  number={3},
  year={2011}
}

@inproceedings{bassily2014private,
  title={Private empirical risk minimization: Efficient algorithms and tight error bounds},
  author={Bassily, Raef and Smith, Adam and Thakurta, Abhradeep},
  booktitle={2014 IEEE 55th annual symposium on foundations of computer science},
  pages={464--473},
  year={2014},
  organization={IEEE}
}

@article{wang2017differentially,
  title={Differentially private empirical risk minimization revisited: Faster and more general},
  author={Wang, Di and Ye, Minwei and Xu, Jinhui},
  journal={Advances in Neural Information Processing Systems},
  volume={30},
  year={2017}
}

@inproceedings{feldman2020private,
  title={Private stochastic convex optimization: optimal rates in linear time},
  author={Feldman, Vitaly and Koren, Tomer and Talwar, Kunal},
  booktitle={Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing},
  pages={439--449},
  year={2020}
}

@article{andrew2021differentially,
  title={Differentially private learning with adaptive clipping},
  author={Andrew, Galen and Thakkar, Om and McMahan, Brendan and Ramaswamy, Swaroop},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={17455--17466},
  year={2021}
}

@inproceedings{asi2021private,
  title={Private adaptive gradient methods for convex optimization},
  author={Asi, Hilal and Duchi, John and Fallah, Alireza and Javidbakht, Omid and Talwar, Kunal},
  booktitle={International Conference on Machine Learning},
  pages={383--392},
  year={2021},
  organization={PMLR}
}

@article{tsigler2023benign,
  title={Benign overfitting in ridge regression.},
  author={Tsigler, Alexander and Bartlett, Peter L},
  journal={J. Mach. Learn. Res.},
  volume={24},
  pages={123--1},
  year={2023}
}

@inproceedings{defossez2015averaged,
  title={Averaged least-mean-squares: Bias-variance trade-offs and optimal sampling distributions},
  author={D{\'e}fossez, Alexandre and Bach, Francis},
  booktitle={Artificial Intelligence and Statistics},
  pages={205--213},
  year={2015},
  organization={PMLR}
}

@article{dieuleveut2017harder,
  title={Harder, better, faster, stronger convergence rates for least-squares regression},
  author={Dieuleveut, Aymeric and Flammarion, Nicolas and Bach, Francis},
  journal={The Journal of Machine Learning Research},
  volume={18},
  number={1},
  pages={3520--3570},
  year={2017},
  publisher={JMLR. org}
}

@article{jain2017markov,
  title={A markov chain theory approach to characterizing the minimax optimality of stochastic gradient descent (for least squares)},
  author={Jain, Prateek and Kakade, Sham M and Kidambi, Rahul and Netrapalli, Praneeth and Pillutla, Venkata Krishna and Sidford, Aaron},
  journal={arXiv preprint arXiv:1710.09430},
  year={2017}
}

@article{chen2020dimension,
  title={Dimension independent generalization error by stochastic gradient descent},
  author={Chen, Xi and Liu, Qiang and Tong, Xin T},
  journal={arXiv preprint arXiv:2003.11196},
  year={2020}
}

@inproceedings{wu2022last,
  title={Last iterate risk bounds of sgd with decaying stepsize for overparameterized linear regression},
  author={Wu, Jingfeng and Zou, Difan and Braverman, Vladimir and Gu, Quanquan and Kakade, Sham},
  booktitle={International Conference on Machine Learning},
  pages={24280--24314},
  year={2022},
  organization={PMLR}
}

@article{agarap2018deep,
  title={Deep learning using rectified linear units (relu)},
  author={Agarap, Abien Fred},
  journal={arXiv preprint arXiv:1803.08375},
  year={2018}
}

@article{manurangsi2018computational,
  title={The computational complexity of training relu (s)},
  author={Manurangsi, Pasin and Reichman, Daniel},
  journal={arXiv preprint arXiv:1810.04207},
  year={2018}
}

@article{goel2019time,
  title={Time/accuracy tradeoffs for learning a relu with respect to gaussian marginals},
  author={Goel, Surbhi and Karmalkar, Sushrut and Klivans, Adam},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{diakonikolas2020approximation,
  title={Approximation schemes for relu regression},
  author={Diakonikolas, Ilias and Goel, Surbhi and Karmalkar, Sushrut and Klivans, Adam R and Soltanolkotabi, Mahdi},
  booktitle={Conference on learning theory},
  pages={1452--1485},
  year={2020},
  organization={PMLR}
}

@article{frei2020agnostic,
  title={Agnostic learning of a single neuron with gradient descent},
  author={Frei, Spencer and Cao, Yuan and Gu, Quanquan},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={5417--5428},
  year={2020}
}

@inproceedings{diakonikolas2022learning,
  title={Learning a single neuron with adversarial label noise via gradient descent},
  author={Diakonikolas, Ilias and Kontonis, Vasilis and Tzamos, Christos and Zarifis, Nikos},
  booktitle={Conference on Learning Theory},
  pages={4313--4361},
  year={2022},
  organization={PMLR}
}

@inproceedings{wang2019sparse,
  title={On sparse linear regression in the local differential privacy model},
  author={Wang, Di and Xu, Jinhui},
  booktitle={International Conference on Machine Learning},
  pages={6628--6637},
  year={2019},
  organization={PMLR}
}

@article{liu2023near,
  title={Near Optimal Private and Robust Linear Regression},
  author={Liu, Xiyang and Jain, Prateek and Kong, Weihao and Oh, Sewoong and Suggala, Arun Sai},
  journal={arXiv preprint arXiv:2301.13273},
  year={2023}
}

@article{zhu2023improved,
  title={Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model},
  author={Zhu, Liyang and Ding, Meng and Aggarwal, Vaneet and Xu, Jinhui and Wang, Di},
  journal={arXiv preprint arXiv:2310.07367},
  year={2023}
}

@inproceedings{abadi2016deep,
  title={Deep learning with differential privacy},
  author={Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li},
  booktitle={Proceedings of the 2016 ACM SIGSAC conference on computer and communications security},
  pages={308--318},
  year={2016}
}

@article{mcmahan2017learning,
  title={Learning differentially private recurrent language models},
  author={McMahan, H Brendan and Ramage, Daniel and Talwar, Kunal and Zhang, Li},
  journal={arXiv preprint arXiv:1710.06963},
  year={2017}
}

@inproceedings{amin2019bounding,
  title={Bounding user contributions: A bias-variance trade-off in differential privacy},
  author={Amin, Kareem and Kulesza, Alex and Munoz, Andres and Vassilvtiskii, Sergei},
  booktitle={International Conference on Machine Learning},
  pages={263--271},
  year={2019},
  organization={PMLR}
}

@article{erlingsson2019we,
  title={That which we call private},
  author={Erlingsson, {\'U}lfar and Mironov, Ilya and Raghunathan, Ananth and Song, Shuang},
  journal={arXiv preprint arXiv:1908.03566},
  year={2019}
}

@inproceedings{kairouz2021practical,
  title={Practical and private (deep) learning without sampling or shuffling},
  author={Kairouz, Peter and McMahan, Brendan and Song, Shuang and Thakkar, Om and Thakurta, Abhradeep and Xu, Zheng},
  booktitle={International Conference on Machine Learning},
  pages={5213--5225},
  year={2021},
  organization={PMLR}
}

@inproceedings{wang2019differentially,
  title={Differentially private empirical risk minimization with smooth non-convex loss functions: A non-stationary view},
  author={Wang, Di and Xu, Jinhui},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  number={01},
  pages={1182--1189},
  year={2019}
}

@article{bassily2019private,
  title={Private stochastic convex optimization with optimal rates},
  author={Bassily, Raef and Feldman, Vitaly and Talwar, Kunal and Guha Thakurta, Abhradeep},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}


@article{song2020characterizing,
  title={Characterizing private clipped gradient descent on convex generalized linear problems},
  author={Song, Shuang and Thakkar, Om and Thakurta, Abhradeep},
  journal={arXiv preprint arXiv:2006.06783},
  year={2020}
}

@article{su2021faster,
  title={Faster rates of differentially private stochastic convex optimization},
  author={Su, Jinyan and Wang, Di},
  journal={arXiv preprint arXiv},
  volume={2108},
  year={2021}
}


@inproceedings{bassily2021non,
  title={Non-euclidean differentially private stochastic convex optimization},
  author={Bassily, Raef and Guzm{\'a}n, Crist{\'o}bal and Nandi, Anupama},
  booktitle={Conference on Learning Theory},
  pages={474--499},
  year={2021},
  organization={PMLR}
}

@article{kulkarni2021private,
  title={Private non-smooth empirical risk minimization and stochastic convex optimization in subquadratic steps},
  author={Kulkarni, Janardhan and Lee, Yin Tat and Liu, Daogao},
  journal={arXiv preprint arXiv:2103.15352},
  year={2021}
}

%non-convex

@article{zhang2017efficient,
  title={Efficient private ERM for smooth objectives},
  author={Zhang, Jiaqi and Zheng, Kai and Mou, Wenlong and Wang, Liwei},
  journal={arXiv preprint arXiv:1703.09947},
  year={2017}
}


@inproceedings{zhang2021private,
  title={Private stochastic non-convex optimization with improved utility rates},
  author={Zhang, Qiuchen and Ma, Jing and Lou, Jian and Xiong, Li},
  booktitle={Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence},
  year={2021}
}

@inproceedings{wang2023efficient,
  title={Efficient privacy-preserving stochastic nonconvex optimization},
  author={Wang, Lingxiao and Jayaraman, Bargav and Evans, David and Gu, Quanquan},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={2203--2213},
  year={2023},
  organization={PMLR}
}

@article{bassily2021differentially,
  title={Differentially private stochastic optimization: New results in convex and non-convex settings},
  author={Bassily, Raef and Guzm{\'a}n, Crist{\'o}bal and Menart, Michael},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={9317--9329},
  year={2021}
}

@article{chan2011private,
  title={Private and continual release of statistics},
  author={Chan, T-H Hubert and Shi, Elaine and Song, Dawn},
  journal={ACM Transactions on Information and System Security (TISSEC)},
  volume={14},
  number={3},
  pages={1--24},
  year={2011},
  publisher={ACM New York, NY, USA}
}

@inproceedings{dwork2010differential,
  title={Differential privacy under continual observation},
  author={Dwork, Cynthia and Naor, Moni and Pitassi, Toniann and Rothblum, Guy N},
  booktitle={Proceedings of the forty-second ACM symposium on Theory of computing},
  pages={715--724},
  year={2010}
}

@article{tran2022momentum,
  title={Momentum aggregation for private non-convex erm},
  author={Tran, Hoang and Cutkosky, Ashok},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={10996--11008},
  year={2022}
}

@article{duchi2011adaptive,
  title={Adaptive subgradient methods for online learning and stochastic optimization.},
  author={Duchi, John and Hazan, Elad and Singer, Yoram},
  journal={Journal of machine learning research},
  volume={12},
  number={7},
  year={2011}
}

@inproceedings{mcmahan2011follow,
  title={Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization},
  author={McMahan, Brendan},
  booktitle={Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
  pages={525--533},
  year={2011},
  organization={JMLR Workshop and Conference Proceedings}
}

@inproceedings{zhu2019poission,
  title={Poission subsampled r{\'e}nyi differential privacy},
  author={Zhu, Yuqing and Wang, Yu-Xiang},
  booktitle={International Conference on Machine Learning},
  pages={7634--7642},
  year={2019},
  organization={PMLR}
}

@inproceedings{agarwal2023differentially,
  title={Differentially private and lazy online convex optimization},
  author={Agarwal, Naman and Kale, Satyen and Singh, Karan and Thakurta, Abhradeep},
  booktitle={The Thirty Sixth Annual Conference on Learning Theory},
  pages={4599--4632},
  year={2023},
  organization={PMLR}
}

@article{karwa2017finite,
  title={Finite sample differentially private confidence intervals},
  author={Karwa, Vishesh and Vadhan, Salil},
  journal={arXiv preprint arXiv:1711.03908},
  year={2017}
}

@inproceedings{song2021evading,
  title={Evading the curse of dimensionality in unconstrained private glms},
  author={Song, Shuang and Steinke, Thomas and Thakkar, Om and Thakurta, Abhradeep},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={2638--2646},
  year={2021},
  organization={PMLR}
}

@article{zhou2020bypassing,
  title={Bypassing the ambient dimension: Private sgd with gradient subspace identification},
  author={Zhou, Yingxue and Wu, Zhiwei Steven and Banerjee, Arindam},
  journal={arXiv preprint arXiv:2007.03813},
  year={2020}
}

@inproceedings{xiao2023theory,
  title={A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction},
  author={Xiao, Hanshen and Xiang, Zihang and Wang, Di and Devadas, Srinivas},
  booktitle={2023 IEEE Symposium on Security and Privacy (SP)},
  pages={2170--2189},
  year={2023},
  organization={IEEE Computer Society}
}

@inproceedings{agarwal2017finding,
  title={Finding approximate local minima faster than gradient descent},
  author={Agarwal, Naman and Allen-Zhu, Zeyuan and Bullins, Brian and Hazan, Elad and Ma, Tengyu},
  booktitle={Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing},
  pages={1195--1199},
  year={2017}
}

@inproceedings{wang2021escaping,
  title={Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method},
  author={Wang, Di and Xu, Jinhui},
  booktitle={Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14--18, 2020, Proceedings, Part III},
  pages={90--106},
  year={2021},
  organization={Springer}
}

@inproceedings{jain2014near,
  title={(Near) dimension independent risk bounds for differentially private learning},
  author={Jain, Prateek and Thakurta, Abhradeep Guha},
  booktitle={International Conference on Machine Learning},
  pages={476--484},
  year={2014},
  organization={PMLR}
}

@inproceedings{kasiviswanathan2016efficient,
  title={Efficient private empirical risk minimization for high-dimensional learning},
  author={Kasiviswanathan, Shiva Prasad and Jin, Hongxia},
  booktitle={International Conference on Machine Learning},
  pages={488--497},
  year={2016},
  organization={PMLR}
}

@article{arora2022differentially,
  title={Differentially private generalized linear models revisited},
  author={Arora, Raman and Bassily, Raef and Guzm{\'a}n, Crist{\'o}bal and Menart, Michael and Ullah, Enayat},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={22505--22517},
  year={2022}
}

@inproceedings{hu2022high,
  title={High dimensional differentially private stochastic optimization with heavy-tailed data},
  author={Hu, Lijie and Ni, Shuo and Xiao, Hanshen and Wang, Di},
  booktitle={Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems},
  pages={227--236},
  year={2022}
}


@inproceedings{goel2019learning,
  title={Learning neural networks with two nonlinear layers in polynomial time},
  author={Goel, Surbhi and Klivans, Adam R},
  booktitle={Conference on Learning Theory},
  pages={1470--1499},
  year={2019},
  organization={PMLR}
}

@article{rudelson2013hanson,
  title={Hanson-wright inequality and sub-gaussian concentration},
  author={Rudelson, Mark and Vershynin, Roman},
  year={2013}
}

@article{cai2021cost,
  title={The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy},
  author={Cai, T Tony and Wang, Yichen and Zhang, Linjun},
  journal={The Annals of Statistics},
  volume={49},
  number={5},
  pages={2825--2850},
  year={2021},
  publisher={Institute of Mathematical Statistics}
}

@article{du2018gradient,
  title={Gradient descent provably optimizes over-parameterized neural networks},
  author={Du, Simon S and Zhai, Xiyu and Poczos, Barnabas and Singh, Aarti},
  journal={arXiv preprint arXiv:1810.02054},
  year={2018}
}

@misc{wine_quality_186,
  author       = {Cortez, Paulo and Cerdeira, A. and Almeida, F. and Matos, T. and Reis, J.},
  title        = {{Wine Quality}},
  year         = {2009},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: https://doi.org/10.24432/C56S3T}
}

@misc{gas_turbine_co_and_nox_emission_data_set_551,
  title        = {{Gas Turbine CO and NOx Emission Data Set}},
  year         = {2019},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: https://doi.org/10.24432/C5WC95}
}

@misc{california_housing_1997,
  author       = {Pace, R. Kelley and Barry, Ronald},
  title        = {California Housing Data},
  year         = {1997},
  howpublished = {StatLib Repository},
  note         = {Data obtained from the 1990 U.S. Census. The manuscript describing the data can be found at \url{http://www.spatial-statistics.com}. DOI: https://doi.org/10.1016/S0167-7152(97)00107-X},
  url          = {http://www.spatial-statistics.com},
}

@inproceedings{dingrevisiting,
  title={Revisiting Differentially Private ReLU Regression},
  author={Ding, Meng and Lei, Mingxi and Zhu, Liyang and Wang, Shaowei and Wang, Di and Xu, Jinhui},
  booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems}
}

@inproceedings{jain2012differentially,
  title={Differentially private online learning},
  author={Jain, Prateek and Kothari, Pravesh and Thakurta, Abhradeep},
  booktitle={Conference on Learning Theory},
  pages={24--1},
  year={2012},
  organization={JMLR Workshop and Conference Proceedings}
}

@inproceedings{kifer2012private,
  title={Private convex empirical risk minimization and high-dimensional regression},
  author={Kifer, Daniel and Smith, Adam and Thakurta, Abhradeep},
  booktitle={Conference on Learning Theory},
  pages={25--1},
  year={2012},
  organization={JMLR Workshop and Conference Proceedings}
}

@inproceedings{iyengar2019towards,
  title={Towards practical differentially private convex optimization},
  author={Iyengar, Roger and Near, Joseph P and Song, Dawn and Thakkar, Om and Thakurta, Abhradeep and Wang, Lun},
  booktitle={2019 IEEE symposium on security and privacy (SP)},
  pages={299--316},
  year={2019},
  organization={IEEE}
}

@inproceedings{wu2017bolt,
  title={Bolt-on differential privacy for scalable stochastic gradient descent-based analytics},
  author={Wu, Xi and Li, Fengan and Kumar, Arun and Chaudhuri, Kamalika and Jha, Somesh and Naughton, Jeffrey},
  booktitle={Proceedings of the 2017 ACM International Conference on Management of Data},
  pages={1307--1322},
  year={2017}
}

@article{talwar2014private,
  title={Private empirical risk minimization beyond the worst case: The effect of the constraint set geometry},
  author={Talwar, Kunal and Thakurta, Abhradeep and Zhang, Li},
  journal={arXiv preprint arXiv:1411.5417},
  year={2014}
}

@article{jayaraman2018distributed,
  title={Distributed learning without distress: Privacy-preserving empirical risk minimization},
  author={Jayaraman, Bargav and Wang, Lingxiao and Evans, David and Gu, Quanquan},
  journal={Advances in Neural Information Processing Systems},
  volume={31},
  year={2018}
}