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2020 – today
- 2024
[c39]Tingting Ou, Rachel Cummings, Marco Avella Medina:
Thompson Sampling Itself is Differentially Private. AISTATS 2024: 1576-1584- [c38]Sebastian Benthall
, Rachel Cummings:
Integrating Differential Privacy and Contextual Integrity. CSLAW 2024: 9-15 - [i36]Sebastian Benthall, Rachel Cummings:
Integrating Differential Privacy and Contextual Integrity. CoRR abs/2401.15774 (2024) - [i35]Rachel Cummings, Shlomi Hod, Jayshree Sarathy, Marika Swanberg:
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries. CoRR abs/2405.01716 (2024) - [i34]Rachel Cummings, Jayshree Sarathy:
Centering Policy and Practice: Research Gaps around Usable Differential Privacy. CoRR abs/2406.12103 (2024) - [i33]Tingting Ou, Marco Avella Medina, Rachel Cummings:
Thompson Sampling Itself is Differentially Private. CoRR abs/2407.14879 (2024) - [i32]Mary Anne Smart, Priyanka Nanayakkara, Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles:
Models Matter: Setting Accurate Privacy Expectations for Local and Central Differential Privacy. CoRR abs/2408.08475 (2024) - 2023
- [j10]Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles:
"I need a better description": An Investigation Into User Expectations For Differential Privacy. J. Priv. Confidentiality 13(1) (2023) - [c37]Saeyoung Rho, Rachel Cummings, Vishal Misra:
Differentially Private Synthetic Control. AISTATS 2023: 1457-1491 - [c36]Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub:
An active learning framework for multi-group mean estimation. NeurIPS 2023 - [c35]Rachel Cummings, Hadi Elzayn, Emmanouil Pountourakis, Vasilis Gkatzelis, Juba Ziani:
Optimal Data Acquisition with Privacy-Aware Agents. SaTML 2023: 210-224 - [c34]Inbal Dekel, Rachel Cummings, Ori Heffetz, Katrina Ligett:
The Privacy Elasticity of Behavior: Conceptualization and Application. EC 2023: 516 - [c33]Rachel Cummings, Jayshree Sarathy:
Centering Policy and Practice: Research Gaps Around Usable Differential Privacy. TPS-ISA 2023: 122-135 - [c32]Priyanka Nanayakkara, Mary Anne Smart, Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles:
What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy. USENIX Security Symposium 2023: 1613-1630 - [i31]Sloan Nietert, Rachel Cummings, Ziv Goldfeld:
Robust Estimation under the Wasserstein Distance. CoRR abs/2302.01237 (2023) - [i30]Priyanka Nanayakkara, Mary Anne Smart, Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles:
What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy. CoRR abs/2303.00738 (2023) - [i29]Saeyoung Rho, Rachel Cummings, Vishal Misra:
Differentially Private Synthetic Control. CoRR abs/2303.14084 (2023) - [i28]Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang:
Challenges towards the Next Frontier in Privacy. CoRR abs/2304.06929 (2023) - [i27]Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar:
Mean Estimation with User-level Privacy under Data Heterogeneity. CoRR abs/2307.15835 (2023) - 2022
- [c31]Qiaomei Li, Rachel Cummings, Yonatan Mintz:
Optimal Local Explainer Aggregation for Interpretable Prediction. AAAI 2022: 12000-12007 - [c30]Wanrong Zhang, Yajun Mei, Rachel Cummings:
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size. AISTATS 2022: 11356-11373 - [c29]Sloan Nietert, Ziv Goldfeld, Rachel Cummings:
Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis. AISTATS 2022: 11691-11719 - [c28]Wanrong Zhang, Olga Ohrimenko, Rachel Cummings:
Attribute Privacy: Framework and Mechanisms. FAccT 2022: 757-766 - [c27]Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar:
Mean Estimation with User-level Privacy under Data Heterogeneity. NeurIPS 2022 - [i26]Wanrong Zhang, Yajun Mei, Rachel Cummings:
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size. CoRR abs/2204.04597 (2022) - [i25]Rachel Cummings, Hadi Elzayn, Vasilis Gkatzelis, Emmanouil Pountourakis, Juba Ziani:
Optimal Data Acquisition with Privacy-Aware Agents. CoRR abs/2209.06340 (2022) - 2021
- [j9]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, 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, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, 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. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [j8]Uthaipon Tao Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, Rachel Cummings:
Differentially private synthetic mixed-type data generation for unsupervised learning. Intell. Decis. Technol. 15(4): 779-807 (2021) - [j7]Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings:
Single and Multiple Change-Point Detection with Differential Privacy. J. Mach. Learn. Res. 22: 29:1-29:36 (2021) - [c26]Chris Waites, Rachel Cummings:
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation. AIES 2021: 1000-1009 - [c25]Sebastian Perez-Salazar, Rachel Cummings:
Differentially Private Online Submodular Maximization. AISTATS 2021: 1279-1287 - [c24]Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles:
"I need a better description": An Investigation Into User Expectations For Differential Privacy. CCS 2021: 3037-3052 - [c23]Wanrong Zhang, Gautam Kamath, Rachel Cummings:
PAPRIKA: Private Online False Discovery Rate Control. ICML 2021: 12458-12467 - [c22]Uthaipon Tao Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, Rachel Cummings:
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning. IISA 2021: 1-9 - [i24]Chris Waites, Rachel Cummings:
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation. CoRR abs/2103.14068 (2021) - [i23]Yuliia Lut, Michael Wang, Elissa M. Redmiles, Rachel Cummings:
How we browse: Measurement and analysis of digital behavior. CoRR abs/2108.06745 (2021) - [i22]Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles:
"I need a better description": An Investigation Into User Expectations For Differential Privacy. CoRR abs/2110.06452 (2021) - [i21]Sloan Nietert, Rachel Cummings, Ziv Goldfeld:
Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis. CoRR abs/2111.01361 (2021) - 2020
- [j6]Grace Bang, Guy Barash, Ryan Beal, Jacques Calì, Mauricio Castillo-Effen, Xin Cynthia Chen, Niyati Chhaya, Rachel Cummings, Rohan Dhoopar, Sebastijan Dumancic, Huáscar Espinoza, Eitan Farchi, Ferdinando Fioretto, Raquel Fuentetaja, Christopher William Geib, Odd Erik Gundersen, José Hernández-Orallo, Xiaowei Huang, Kokil Jaidka, Sarah Keren, Seokhwan Kim, Michel Galley, Xiaomo Liu, Tyler Lu, Zhiqiang Ma, Richard Mallah, John A. McDermid, Martin Michalowski, Reuth Mirsky, Seán Ó hÉigeartaigh, Deepak Ramachandran, Javier Segovia-Aguas, Onn Shehory, Arash Shaban-Nejad, Vered Shwartz, Siddharth Srivastava, Kartik Talamadupula, Jian Tang, Pascal Van Hentenryck, Dell Zhang, Jian Zhang:
The Association for the Advancement of Artificial Intelligence 2020 Workshop Program. AI Mag. 41(4): 100-114 (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 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, Kallista A. 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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last updated on 2024-10-07 21:21 CEST by the dblp team
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