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ORCIDRachel Cummings
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2020 – today
- 2020
[j5]Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan:
The Possibilities and Limitations of Private Prediction Markets. ACM Trans. Economics and Comput. 8(3): 15:1-15:24 (2020)- [c21]Rachel Cummings, Michael Hay:
TPDP'20: 6th Workshop on Theory and Practice of Differential Privacy. CCS 2020: 2153-2154 - [c20]Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang:
Privately detecting changes in unknown distributions. ICML 2020: 2227-2237 - [c19]Rachel Cummings, David Durfee:
Individual Sensitivity Preprocessing for Data Privacy. SODA 2020: 528-547 - [c18]Rachel Cummings, Nikhil R. Devanur, Zhiyi Huang, Xiangning Wang:
Algorithmic Price Discrimination. SODA 2020: 2432-2451 - [i20]Wanrong Zhang, Gautam Kamath, Rachel Cummings:
PAPRIKA: Private Online False Discovery Rate Control. CoRR abs/2002.12321 (2020) - [i19]Qiaomei Li, Rachel Cummings, Yonatan Mintz:
Locally Interpretable Predictions of Parkinson's Disease Progression. CoRR abs/2003.09466 (2020) - [i18]Wanrong Zhang, Olga Ohrimenko, Rachel Cummings:
Attribute Privacy: Framework and Mechanisms. CoRR abs/2009.04013 (2020) - [i17]Sebastian Perez-Salazar, Rachel Cummings:
Differentially Private Online Submodular Maximization. CoRR abs/2010.12816 (2020)
2010 – 2019
- 2019
- [c17]Adrian Rivera Cardoso, Rachel Cummings:
Differentially Private Online Submodular Minimization. AISTATS 2019: 1650-1658 - [c16]Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie H. Morgenstern:
Learning Auctions with Robust Incentive Guarantees. NeurIPS 2019: 11587-11597 - [c15]Rachel Cummings, Varun Gupta, Dhamma Kimpara, Jamie Morgenstern:
On the Compatibility of Privacy and Fairness. UMAP (Adjunct Publication) 2019: 309-315 - [i16]Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang:
Privately detecting changes in unknown distributions. CoRR abs/1910.01327 (2019) - [i15]Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, Rachel Cummings:
Differentially Private Mixed-Type Data Generation For Unsupervised Learning. CoRR abs/1912.03250 (2019) - [i14]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - [i13]Rachel Cummings, Nikhil R. Devanur, Zhiyi Huang, Xiangning Wang:
Algorithmic Price Discrimination. CoRR abs/1912.05770 (2019) - 2018
- [c14]Rachel Cummings, Sara Krehbiel, Kevin A. Lai, Uthaipon Tao Tantipongpipat:
Differential Privacy for Growing Databases. NeurIPS 2018: 8878-8887 - [c13]Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang:
Differentially Private Change-Point Detection. NeurIPS 2018: 10848-10857 - [i12]Rachel Cummings, Sara Krehbiel, Kevin A. Lai, Uthaipon Tantipongpipat:
Differential Privacy for Growing Databases. CoRR abs/1803.06416 (2018) - [i11]Rachel Cummings, David Durfee:
Individual Sensitivity Preprocessing for Data Privacy. CoRR abs/1804.08645 (2018) - [i10]Adrian Rivera Cardoso, Rachel Cummings:
Differentially Private Online Submodular Optimization. CoRR abs/1807.02290 (2018) - [i9]Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang:
Differentially Private Change-Point Detection. CoRR abs/1808.10056 (2018) - 2017
- [j4]Rachel Cummings:
Differential privacy as a tool for truthfulness in games. XRDS 24(1): 34-37 (2017) - [j3]Ho-Lin Chen
, Rachel Cummings, David Doty, David Soloveichik:
Speed faults in computation by chemical reaction networks. Distributed Comput. 30(5): 373-390 (2017) - 2016
- [j2]Rachel Cummings, Federico Echenique, Adam Wierman:
The Empirical Implications of Privacy-Aware Choice. Oper. Res. 64(1): 67-78 (2016) - [j1]Rachel Cummings, David Doty, David Soloveichik:
Probability 1 computation with chemical reaction networks. Nat. Comput. 15(2): 245-261 (2016) - [c12]Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu:
Adaptive Learning with Robust Generalization Guarantees. COLT 2016: 772-814 - [c11]Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu:
Coordination Complexity: Small Information Coordinating Large Populations. ITCS 2016: 281-290 - [c10]Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan:
The Possibilities and Limitations of Private Prediction Markets. EC 2016: 143-160 - [c9]Rachel Cummings, Katrina Ligett, Mallesh M. Pai, Aaron Roth:
The Strange Case of Privacy in Equilibrium Models. EC 2016: 659 - [i8]Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan:
The Possibilities and Limitations of Private Prediction Markets. CoRR abs/1602.07362 (2016) - [i7]Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu:
Adaptive Learning with Robust Generalization Guarantees. CoRR abs/1602.07726 (2016) - 2015
- [c8]Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth:
Online Learning and Profit Maximization from Revealed Preferences. AAAI 2015: 770-776 - [c7]Rachel Cummings, Stratis Ioannidis, Katrina Ligett:
Truthful Linear Regression. COLT 2015: 448-483 - [c6]Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, Juba Ziani:
Accuracy for Sale: Aggregating Data with a Variance Constraint. ITCS 2015: 317-324 - [c5]Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Privacy and Truthful Equilibrium Selection for Aggregative Games. WINE 2015: 286-299 - [i6]Rachel Cummings, Stratis Ioannidis, Katrina Ligett:
Truthful Linear Regression. CoRR abs/1506.03489 (2015) - [i5]Rachel Cummings, Katrina Ligett, Mallesh M. Pai, Aaron Roth:
The Strange Case of Privacy in Equilibrium Models. CoRR abs/1508.03080 (2015) - [i4]Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu:
Coordination Complexity: Small Information Coordinating Large Populations. CoRR abs/1508.03735 (2015) - 2014
- [c4]Rachel Cummings, David Doty, David Soloveichik:
Probability 1 Computation with Chemical Reaction Networks. DNA 2014: 37-52 - [c3]Rachel Cummings, Federico Echenique, Adam Wierman:
The empirical implications of privacy-aware choice. EC 2014: 969 - [c2]Ho-Lin Chen, Rachel Cummings, David Doty, David Soloveichik:
Speed Faults in Computation by Chemical Reaction Networks. DISC 2014: 16-30 - [i3]Rachel Cummings, Federico Echenique, Adam Wierman:
The Empirical Implications of Privacy-Aware Choice. CoRR abs/1401.0336 (2014) - [i2]Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth:
Online Learning and Profit Maximization from Revealed Preferences. CoRR abs/1407.7294 (2014) - [i1]Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Privacy and Truthful Equilibrium Selection for Aggregative Games. CoRR abs/1407.7740 (2014) - 2011
- [c1]Wei Chen, Alex Collins, Rachel Cummings, Te Ke, Zhenming Liu, David Rincón, Xiaorui Sun, Yajun Wang, Wei Wei, Yifei Yuan:
Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate. SDM 2011: 379-390
Coauthor Index
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