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@Article{Samuel59,
  author  = {A. L. Samuel},
  title   = {Some Studies in Machine Learning Using the Game of Checkers},
  journal = {IBM Journal of Research and Development},
  year    = {1959},
  volume  = {3},
  number  = {3},
  pages   = {211--229},
}

@inproceedings{10.1145/2591796.2591826,
author = {Hsu, Justin and Huang, Zhiyi and Roth, Aaron and Roughgarden, Tim and Wu, Zhiwei Steven},
title = {Private matchings and allocations},
year = {2014},
isbn = {9781450327107},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2591796.2591826},
doi = {10.1145/2591796.2591826},
abstract = {We consider a private variant of the classical allocation problem: given k goods and n agents with individual, private valuation functions over bundles of goods, how can we partition the goods amongst the agents to maximize social welfare? An important special case is when each agent desires at most one good, and specifies her (private) value for each good: in this case, the problem is exactly the maximum-weight matching problem in a bipartite graph.Private matching and allocation problems have not been considered in the differential privacy literature, and for good reason: they are plainly impossible to solve under differential privacy. Informally, the allocation must match agents to their preferred goods in order to maximize social welfare, but this preference is exactly what agents wish to hide! Therefore, we consider the problem under the relaxed constraint of joint differential privacy: for any agent i, no coalition of agents excluding i should be able to learn about the valuation function of agent i. In this setting, the full allocation is no longer published---instead, each agent is told what good to get. We first show that with a small number of identical copies of each good, it is possible to efficiently and accurately solve the maximum weight matching problem while guaranteeing joint differential privacy. We then consider the more general allocation problem, when bidder valuations satisfy the gross substitutes condition. Finally, we prove that the allocation problem cannot be solved to non-trivial accuracy under joint differential privacy without requiring multiple copies of each type of good.},
booktitle = {Proceedings of the Forty-Sixth Annual ACM Symposium on Theory of Computing},
pages = {21–30},
numpages = {10},
keywords = {ascending auction, differential privacy, gross substitutes, matching},
location = {New York, New York},
series = {STOC '14}
}



@inproceedings{10.1145/2902251.2902291,
author = {Sealfon, Adam},
title = {Shortest Paths and Distances with Differential Privacy},
year = {2016},
isbn = {9781450341912},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2902251.2902291},
doi = {10.1145/2902251.2902291},
abstract = {We introduce a model for differentially private analysis of weighted graphs in which the graph topology (υ,ε) is assumed to be public and the private information consists only of the edge weights ω : ε → R+. This can express hiding congestion patterns in a known system of roads. Differential privacy requires that the output of an algorithm provides little advantage, measured by privacy parameters ε and δ, for distinguishing between neighboring inputs, which are thought of as inputs that differ on the contribution of one individual. In our model, two weight functions w,w' are considered to be neighboring if they have l1 distance at most one.We study the problems of privately releasing a short path between a pair of vertices and of privately releasing approximate distances between all pairs of vertices. We are concerned with the approximation error, the difference between the length of the released path or released distance and the length of the shortest path or actual distance.For the problem of privately releasing a short path between a pair of vertices, we prove a lower bound of Ω(|υ|) on the additive approximation error for fixed privacy parameters ε,δ. We provide a differentially private algorithm that matches this error bound up to a logarithmic factor and releases paths between all pairs of vertices, not just a single pair. The approximation error achieved by our algorithm can be bounded by the number of edges on the shortest path, so we achieve better accuracy than the worst-case bound for pairs of vertices that are connected by a low-weight path consisting of o(|υ|) vertices.For the problem of privately releasing all-pairs distances, we show that for trees we can release all-pairs distances with approximation error $O(log2.5|υ|) for fixed privacy parameters. For arbitrary bounded-weight graphs with edge weights in [0,M] we can brelease all distances with approximation error \~{O}(√>(|υ|M).},
booktitle = {Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems},
pages = {29–41},
numpages = {13},
keywords = {all-pairs distances, differential privacy, shortest paths},
location = {San Francisco, California, USA},
series = {PODS '16}
}


@misc{javanmard2024priorboostadaptivealgorithmlearning,
      title={PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses},
      author={Adel Javanmard and Matthew Fahrbach and Vahab Mirrokni},
      year={2024},
      eprint={2402.04987},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2402.04987},
}



@misc{balle2018privacyamplificationsubsamplingtight,
      title={Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences},
      author={Borja Balle and Gilles Barthe and Marco Gaboardi},
      year={2018},
      eprint={1807.01647},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.01647},
}

@misc{nguyen2020notedifferentiallyprivateclustering,
      title={A note on differentially private clustering with large additive error}, 
      author={Huy L. Nguyen},
      year={2020},
      eprint={2009.13317},
      archivePrefix={arXiv},
      primaryClass={cs.DS},
      url={https://arxiv.org/abs/2009.13317}, 
}

@misc{grnlund2018fastexactkmeanskmedians,
      title={Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D}, 
      author={Allan Grønlund and Kasper Green Larsen and Alexander Mathiasen and Jesper Sindahl Nielsen and Stefan Schneider and Mingzhou Song},
      year={2018},
      eprint={1701.07204},
      archivePrefix={arXiv},
      primaryClass={cs.DS},
      url={https://arxiv.org/abs/1701.07204}, 
}

@article{devroye2020,
      title={The total variation distance between high-dimensional Gaussians}, 
      author={L. Devroye and A. Mehrabian and T. Reddad},
      journal   = {CoRR},
  volume    = {abs/1810.08693},
  year      = {2020},
  url       = {http://arxiv.org/abs/1810.08693}
}
@article{havaldar2023learning,
  title={Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation},
  author={Havaldar, Shreyas and Sharma, Navodita and Sareen, Shubhi and Shanmugam, Karthikeyan and Raghuveer, Aravindan},
  journal={arXiv preprint arXiv:2310.08056},
  year={2023}
}
@inproceedings{
brahmbhatt2023pac,
title={{PAC} Learning Linear Thresholds from Label Proportions},
author={Anand Paresh Brahmbhatt and Rishi Saket and Aravindan Raghuveer},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=5Gw9YkJkFF}
}
@article{Hoeffding,
 URL = {http://www.jstor.org/stable/2282952},
 author = {Wassily Hoeffding},
 journal = {Journal of the American Statistical Association},
 number = {301},
 pages = {13--30},
 title = {Probability Inequalities for Sums of Bounded Random Variables},
 volume = {58},
 year = {1963}
}

@book{lindvall2002lectures,
  title={Lectures on the Coupling Method},
  author={Lindvall, T.},
  isbn={9780486421452},
  lccn={92012811},
  series={Dover Books on Mathematics Series},
  url={https://books.google.co.in/books?id=GUwyU1ypd1wC},
  year={2002},
  publisher={Dover Publications, Incorporated}
}

@article{YCKJC14,
  author    = {F. X. Yu and
                K. Choromanski and
               S. Kumar and
               T. Jebara and
               S. F. Chang},
  title     = {On Learning from Label Proportions},
  journal   = {CoRR},
  volume    = {abs/1402.5902},
  year      = {2014},
  url       = {http://arxiv.org/abs/1402.5902}
}


@inproceedings{WIBB,
  title={Using published medical results and non-homogenous data in rule learning},
  author={Wojtusiak, J. and Irvin, K. and Birerdinc, A. and Baranova, A. V.},
  booktitle={Proc. International Conference on Machine Learning and Applications and Workshops},
  volume={2},
  pages={84--89},
  year={2011},
  organization={IEEE}
}

@inproceedings{R10,
  title={{SVM} classifier estimation from group probabilities},
  author={Rueping, S.},
  booktitle={Proc. ICML},
  year={2010},
  pages ={911--918}
}

@inproceedings{CHR,
  title={Cost-based labeling of groups of mass spectra},
  author={Chen, L. and Huang, Z. and Ramakrishnan, R.},
  booktitle={Proc. ACM SIGMOD International Conference on Management of Data},
  pages={167--178},
  year={2004}
}

@article{DNRS,
  title={Weakly supervised classification in high energy physics},
  author={Dery, L. M. and Nachman, B. and Rubbo, F. and Schwartzman, A.},
  journal={Journal of High Energy Physics},
  volume={2017},
  number={5},
  pages={1--11},
  year={2017},
  publisher={Springer}
}

@inproceedings{FK05,
  author    = {N. de Freitas and
               H. K{\"{u}}ck},
  title     = {Learning about Individuals from Group Statistics},
  booktitle = {Proc. {UAI}},
  pages     = {332--339},
  year      = {2005}
}

@article{HIL13,
  author    = {J. Hern{\'{a}}ndez{-}Gonz{\'{a}}lez and
               I. Inza and
               J. A. Lozano},
  title     = {Learning Bayesian network classifiers from label proportions},
  journal   = {Pattern Recognit.},
  volume    = {46},
  number    = {12},
  pages     = {3425--3440},
  year      = {2013}
}

@inproceedings{MCO07,
  author    = {D. R. Musicant and
               J. M. Christensen and
               J. F. Olson},
  title     = {Supervised Learning by Training on Aggregate Outputs},
  booktitle = {Proc. {ICDM}},
  pages     = {252--261},
  publisher = {{IEEE} Computer Society},
  year      = {2007}
  }
  
  @inproceedings{CLQZ09,
  author    = {S. Chen and
               B. Liu and
               M. Qian and
               C. Zhang},
  title     = {Kernel K-means Based Framework for Aggregate Outputs Classification},
  booktitle = {Proc. {ICDM}},
  pages     = {356--361},
  year      = {2009}
 }
 
 
  
 @inproceedings{SM11,
  author    = {M. Stolpe and
               K. Morik},
  title     = {Learning from Label Proportions by Optimizing Cluster Model Selection},
  booktitle = { {ECML} {PKDD} Proceedings,
               Part {III}},
  volume    = {6913},
  pages     = {349--364},
  publisher = {Springer},
  year      = {2011}
  }
  
  @article{QSCL09,
  author    = {N. Quadrianto and
               A. J. Smola and
               T. S. Caetano and
               Q. V. Le},
  title     = {Estimating Labels from Label Proportions},
  journal   = {J. Mach. Learn. Res.},
  volume    = {10},
  pages     = {2349--2374},
  year      = {2009}
  }
  
  @inproceedings{PNCR14,
  author    = {G. Patrini and
               R. Nock and
               T. S. Caetano and
               P. Rivera},
  title     = {(Almost) No Label No Cry},
  booktitle = {Proc. Advances in Neural Information Processing Systems},
  pages     = {190--198},
  year      = {2014}
  }
  
   
 @inproceedings{KDFS15,
  author    = {D. Kotzias and
               M. Denil and
               N. de Freitas and
               P. Smyth},
  title     = {From Group to Individual Labels Using Deep Features},
  booktitle = {Proc. SIGKDD},
  pages     = {597--606},
  year      = {2015}
  }
  
  @inproceedings{LWQTS19,
  author    = {J. Liu and
               B. Wang and
               Z. Qi and
               Y. Tian and
               Y. Shi},
  title     = {Learning from Label Proportions with Generative Adversarial Networks},
  booktitle = {Proc. {NeurIPS}},
  pages     = {7167--7177},
  year      = {2019}
}

@article{DZCBV19,
  author    = {G. Dulac{-}Arnold and
               N. Zeghidour and
               M. Cuturi and
               L. Beyer and
               J. P. Vert},
  title     = {Deep multi-class learning from label proportions},
  journal   = {CoRR},
  volume    = {abs/1905.12909},
  year      = {2019},
  url       = {http://arxiv.org/abs/1905.12909}
}


@inproceedings{SZ20,
  author    = {C. Scott and
               J. Zhang},
  title     = {Learning from Label Proportions: {A} Mutual Contamination Framework},
  booktitle = {Proc. NeurIPS},
  year      = {2020}
  }
  
  @inproceedings{ZWS22,
  author    = {J. Zhang, Y. Wang, C. Scott},
  title     = {Learning from Label Proportions by Learning with Label Noise},
  booktitle = {Proc. NeurIPS},
  year      = {2022}
  }
  
  @inproceedings{FR17,
  author    = {B. Fish and
               L. Reyzin},
  title     = {On the Complexity of Learning from Label Proportions},
  booktitle = {Proc. {IJCAI}},
  pages     = {1675--1681},
  year      = {2017}
  }
  
  @inproceedings{YLKJC13,
  author    = {F. X. Yu and
               D. Liu and
               S. Kumar and
               T. Jebara and
               S. F. Chang},
  title     = {$\propto${SVM} for Learning
               with Label Proportions},
  booktitle = {Proc. ICML},
  volume    = {28},
  pages     = {504--512},
  year      = {2013}
  }
  
  @article{AK98,
  author    = {E. Amaldi and
               V. Kann},
  title     = {On the Approximability of Minimizing Nonzero Variables or Unsatisfied
               Relations in Linear Systems},
  journal   = {Theor. Comput. Sci.},
  volume    = {209},
  number    = {1-2},
  pages     = {237--260},
  year      = {1998}
  }
  
    @inproceedings{SRR,
  author    = {Rishi Saket and
               Aravindan Raghuveer and
               Balaraman Ravindran},
  title     = {On Combining Bags to Better Learn from Label Proportions},
  booktitle = {{AISTATS}},
  series    = {Proceedings of Machine Learning Research},
  volume    = {151},
  pages     = {5913--5927},
  publisher = {{PMLR}},
  year      = {2022},
  url       = {https://proceedings.mlr.press/v151/saket22a.html}
}

@inproceedings{NSJCRR22,
  author    = {J. Nandy and
               R. Saket and
               P. Jain and
               J. Chauhan and
               B. Ravindran and
               A. Raghuveer},
  title     = {Domain-Agnostic Contrastive Representations for Learning from Label
               Proportions},
  booktitle = {Proc. CIKM},
  pages     = {1542--1551},
  year      = {2022}
  }
  
  @article{o2022challenges,
  title={Challenges and approaches to privacy preserving post-click conversion prediction},
  author={O'Brien, Conor and Thiagarajan, Arvind and Das, Sourav and Barreto, Rafael and Verma, Chetan and Hsu, Tim and Neufield, James and Hunt, Jonathan J},
  journal={arXiv preprint arXiv:2201.12666},
  year={2022}
}


@inproceedings{dwork2006differential,
  title={Differential privacy},
  author={Dwork, Cynthia},
  booktitle={Automata, Languages and Programming: 33rd International Colloquium, ICALP 2006, Venice, Italy, July 10-14, 2006, Proceedings, Part II 33},
  pages={1--12},
  year={2006},
  organization={Springer}
}

@article{dwork2014algorithmic,
  title={The algorithmic foundations of differential privacy},
  author={Dwork, Cynthia and Roth, Aaron and others},
  journal={Foundations and Trends{\textregistered} in Theoretical Computer Science},
  volume={9},
  number={3--4},
  pages={211--407},
  year={2014},
  publisher={Now Publishers, Inc.}
}

@inproceedings{chaudhuri2011sample,
  title={Sample complexity bounds for differentially private learning},
  author={Chaudhuri, Kamalika and Hsu, Daniel},
  booktitle={Proceedings of the 24th Annual Conference on Learning Theory},
  pages={155--186},
  year={2011},
  organization={JMLR Workshop and Conference Proceedings}
}

@inproceedings{esfandiari2022label,
  title={Label differential privacy via clustering},
  author={Esfandiari, Hossein and Mirrokni, Vahab and Syed, Umar and Vassilvitskii, Sergei},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={7055--7075},
  year={2022},
  organization={PMLR}
}

@article{ghazi2021deep,
  title={Deep learning with label differential privacy},
  author={Ghazi, Badih and Golowich, Noah and Kumar, Ravi and Manurangsi, Pasin and Zhang, Chiyuan},
  journal={Advances in neural information processing systems},
  volume={34},
  pages={27131--27145},
  year={2021}
}

@article{ghazi2022regression,
  title={Regression with Label Differential Privacy},
  author={Ghazi, Badih and Kamath, Pritish and Kumar, Ravi and Leeman, Ethan and Manurangsi, Pasin and Varadarajan, Avinash and Zhang, Chiyuan},
  journal={arXiv preprint arXiv:2212.06074},
  year={2022}
}

@article{malek2021antipodes,
  title={Antipodes of label differential privacy: Pate and alibi},
  author={Malek Esmaeili, Mani and Mironov, Ilya and Prasad, Karthik and Shilov, Igor and Tramer, Florian},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={6934--6945},
  year={2021}
}

@book{goodfellow2016deep,
  title={Deep learning},
  author={Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
  year={2016},
  publisher={MIT press}
}
 
 
 @inproceedings{Saket21,
  author    = {R. Saket},
  title     = {Learnability of Linear Thresholds from Label Proportions},
  booktitle = {Proc. NeurIPS},
  year      = {2021},
  url = {https://openreview.net/forum?id=5BnaKeEwuYk}
  }
  
@inproceedings{Saket22,
  author    = {R. Saket},
  title     = {Algorithms and Hardness for Learning Linear Thresholds from Label Proportions},
  booktitle = {Proc. NeurIPS},
  year      = {2022},
  url = {https://openreview.net/forum?id=4LZo68TuF-4}
  }
  
@book{Vershynin-book, place={Cambridge}, series={Cambridge Series in Statistical and Probabilistic Mathematics}, title={High-Dimensional Probability: An Introduction with Applications in Data Science}, DOI={10.1017/9781108231596}, publisher={Cambridge University Press}, author={Vershynin, Roman}, year={2018}, collection={Cambridge Series in Statistical and Probabilistic Mathematics}}

@inproceedings{tallis2018reacting,
  title={Reacting to variations in product demand: An application for conversion rate ({CR}) prediction in sponsored search},
  author={Tallis, Marcelo and Yadav, Pranjul},
  booktitle={2018 IEEE International Conference on Big Data (Big Data)},
  pages={1856--1864},
  year={2018},
  organization={IEEE}
}

@inproceedings{song2019autoint,
  title={Autoint: Automatic feature interaction learning via self-attentive neural networks},
  author={Song, Weiping and Shi, Chence and Xiao, Zhiping and Duan, Zhijian and Xu, Yewen and Zhang, Ming and Tang, Jian},
  booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
  pages={1161--1170},
  year={2019}
}

@article{chen2023learning,
      title={Learning from Aggregated Data: Curated Bags versus Random Bags}, 
      author={Lin Chen and Thomas Fu and Amin Karbasi and Vahab Mirrokni},
      year={2023},
      journal   = {arXiv},
      url={https://arxiv.org/abs/2305.09557}
}

@article{busafekete2023easy,
      title={Easy Learning from Label Proportions}, 
      author={Robert Istvan Busa-Fekete and Heejin Choi and Travis Dick and Claudio Gentile and Andres Munoz medina},
      year={2023},
      journal   = {arXiv},
      url={https://arxiv.org/abs/2302.03115}
}

@misc{IPUMS-USA,
    author = {Steven Ruggles, Sarah Flood, Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas, and Matthew Sobek},
    title = {{IPUMS USA: Version 8.0 Extract of 1940 Census for U.S. Census Bureau Disclosure Avoidance Research} [dataset]. Minneapolis, {MN}: {IPUMS}},
    year = {2018},
    doi = {https://doi.org/10.18128/D010.V8.0.EXT1940USCB}
}

@article{notesamplingwihtoutreplacement,
  title={Note on sampling without replacing from a finite collection of matrices},
  author={Gross, David and Nesme, Vincent},
  journal={arXiv preprint arXiv:1001.2738},
  year={2010}
}

@inproceedings{graphwithoutreplacement,
  title={Graph approximation and clustering on a budget},
  author={Fetaya, Ethan and Shamir, Ohad and Ullman, Shimon},
  booktitle={Artificial Intelligence and Statistics},
  pages={241--249},
  year={2015},
  organization={PMLR}
}

@article{randommatrixbounds,
  title={User-friendly tail bounds for sums of random matrices},
  author={Tropp, Joel A},
  journal={Foundations of computational mathematics},
  volume={12},
  pages={389--434},
  year={2012},
  publisher={Springer}
}

@article{moshksar2021absolute,
  title={On the absolute constant in hanson-wright inequality},
  author={Moshksar, Kamyar},
  journal={arXiv preprint arXiv:2111.00557},
  year={2021}
}

@article{arbas2023polynomial,
  title={Polynomial time and private learning of unbounded Gaussian Mixture Models},
  author={Arbas, Jamil and Ashtiani, Hassan and Liaw, Christopher},
  journal={arXiv preprint arXiv:2303.04288},
  year={2023}
}




@article{Sheffet-OLS,
  author       = {Or Sheffet},
  title        = {Differentially Private Ordinary Least Squares},
  journal      = {J. Priv. Confidentiality},
  volume       = {9},
  number       = {1},
  year         = {2019}
  }
  
  @inproceedings{Sheffet-old,
  author       = {Or Sheffet},
  title        = {Old Techniques in Differentially Private Linear Regression},
  booktitle    = {Proc. {ALT}},
  series       = {Proceedings of Machine Learning Research},
  volume       = {98},
  pages        = {788--826},
  publisher    = {{PMLR}},
  year         = {2019}
  }
  
  @article{Cai,
author = {T. Tony Cai and Yichen Wang and Linjun Zhang},
title = {{The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy}},
volume = {49},
journal = {The Annals of Statistics},
number = {5},
publisher = {Institute of Mathematical Statistics},
pages = {2825 -- 2850},
year = {2021}
}

@inproceedings{Wang-2,
  author       = {Yu{-}Xiang Wang},
  title        = {Revisiting differentially private linear regression: optimal and adaptive
                  prediction {\&} estimation in unbounded domain},
  booktitle    = {Proc. {UAI}},
  pages        = {93--103},
  year         = {2018}
  }
  
  @article{Alabi,
  author       = {Daniel Alabi and
                  Audra McMillan and
                  Jayshree Sarathy and
                  Adam D. Smith and
                  Salil P. Vadhan},
  title        = {Differentially Private Simple Linear Regression},
  journal      = {Proc. Priv. Enhancing Technol.},
  volume       = {2022},
  number       = {2},
  pages        = {184--204},
  year         = {2022}
  }
  
  @inproceedings{Amin,
  author       = {Kareem Amin and
                  Matthew Joseph and
                  M{\'{o}}nica Ribero and
                  Sergei Vassilvitskii},
  title        = {Easy Differentially Private Linear Regression},
  booktitle    = {Proc. {ICLR}},
  year         = {2023}
  }
  
  @article{rudelson2013hanson,
  title={Hanson-Wright inequality and sub-Gaussian concentration},
  author={Rudelson, Mark and Vershynin, Roman},
  year={2013}
}

@misc{steinke2022compositiondifferentialprivacy,
      title={Composition of Differential Privacy \& Privacy Amplification by Subsampling}, 
      author={Thomas Steinke},
      year={2022},
      eprint={2210.00597},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2210.00597}, 
}

@misc{bebensee2019localdifferentialprivacytutorial,
      title={Local Differential Privacy: a tutorial}, 
      author={Björn Bebensee},
      year={2019},
      eprint={1907.11908},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/1907.11908}, 
}

@misc{su2015differentiallyprivatekmeansclustering,
      title={Differentially Private $k$-Means Clustering}, 
      author={Dong Su and Jianneng Cao and Ninghui Li and Elisa Bertino and Hongxia Jin},
      year={2015},
      eprint={1504.05998},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/1504.05998}, 
}

@article{Lu_2020,
   title={Differentially Private k-Means Clustering with Convergence Guarantee},
   ISSN={2160-9209},
   url={http://dx.doi.org/10.1109/TDSC.2020.3043369},
   DOI={10.1109/tdsc.2020.3043369},
   journal={IEEE Transactions on Dependable and Secure Computing},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Lu, Zhigang and Shen, Hong},
   year={2020},
   pages={1–1} }


@article{van2020survey,
  title={A survey on semi-supervised learning},
  author={Van Engelen, Jesper E and Hoos, Holger H},
  journal={Machine learning},
  volume={109},
  number={2},
  pages={373--440},
  year={2020},
  publisher={Springer}
} 
 @misc{skan,
 title = {{Apple} storekit ad network}, 
 howpublished = {https://developer.apple.com/documentation/storekit/skadnetwork/}
 }
 
 @misc{priv,
 title = {{Private} aggregation api of chrome privacy sandbox},
 howpublished = {https://developer.chrome.com/docs/privacy-sandbox/aggregation-service/}
 }
 
 @inproceedings{RP01,
  author       = {S. Ray and
                  D. Page},
  title        = {Multiple Instance Regression},
  booktitle    = {Proc. {ICML}},
  pages        = {425--432},
  year         = {2001}
  }
  
  @inbook{WRHOV08,
author = {Z. Wang and V. Radosavljevic and B. Han and Z. Obradovic and S. Vucetic},
title = {Aerosol Optical Depth Prediction from Satellite Observations by Multiple Instance Regression},
booktitle = {Proc. (SDM)},
pages = {165-176},
year = {2008}
}

@inproceedings{RC05,
author = {Ray, Soumya and Craven, Mark},
title = {Supervised versus multiple instance learning: an empirical comparison},
year = {2005},
booktitle = {Proc. ICML},
pages = {697–704}
}

@article{tropp2012user,
  title={User-friendly tail bounds for sums of random matrices},
  author={Tropp, Joel A},
  journal={Foundations of computational mathematics},
  volume={12},
  pages={389--434},
  year={2012},
  publisher={Springer}
}

@ARTICLE{WLV7,
  author={Wang, Z. and Lan, L. and Vucetic, S.},
  journal={{IEEE} Transactions on Geoscience and Remote Sensing}, 
  title={Mixture Model for Multiple Instance Regression and Applications in Remote Sensing}, 
  year={2012},
  volume={50},
  number={6},
  pages={2226-2237}
  }
  
  @inproceedings{WLR08,
  author       = {K. L. Wagstaff and
                  T. Lane and
                  A. Roper},
  title        = {Multiple-Instance Regression with Structured Data},
  booktitle    = {Workshops Proceedings of the 8th {IEEE} {ICDM}},
  pages        = {291--300},
  year         = {2008}
  }
  
  @INPROCEEDINGS{TF18,
  author={Trabelsi, Mohamed and Frigui, Hichem},
  booktitle={2018 IEEE International Conference on Fuzzy Systems {(FUZZ-IEEE)}}, 
  title={Fuzzy and Possibilistic Clustering for Multiple Instance Linear Regression}, 
  year={2018},
  volume={},
  number={},
  pages={1-7}
  }
  
  
  @inproceedings{KSABGR,
  author = {Kushal Chauhan and Rishi Saket and  Lorne Applebaum and Ashwinkumar Badanidiyuru and Chandan Giri and Aravindan Raghuveer},
  booktitle = {{UAI}},
  title = {Generalization and Learnability in Multiple Instance Regression},
  year = {2024}
  }
  
  
  
    @inproceedings{ArdehalyC17,
  author    = {Ehsan Mohammady Ardehaly and
               Aron Culotta},
  title     = {Co-Training for Demographic Classification Using Deep Learning from
               Label Proportions},
  booktitle = {{ICDM}},
  pages     = {1017--1024},
  year      = {2017}
  }
