Rachel Cummings

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 70

Top Topics

Unsupervised Learning

Linear Regression

Hypothesis Test

Differential Privacy

Top Venues

Publications

Tingting Ou, Rachel Cummings, Marco Avella
Thompson Sampling Itself is Differentially Private. AISTATS (2024)
Rachel Cummings, Shlomi Hod, Jayshree Sarathy, Marika Swanberg
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries. CoRR (2024)
Sebastian Benthall, Rachel Cummings
Integrating Differential Privacy and Contextual Integrity. CSLAW (2024)
Rachel Cummings, Jayshree Sarathy
Centering Policy and Practice: Research Gaps around Usable Differential Privacy. CoRR (2024)
Tingting Ou, Marco Avella Medina, Rachel Cummings
Thompson Sampling Itself is Differentially Private. CoRR (2024)
Sebastian Benthall, Rachel Cummings
Integrating Differential Privacy and Contextual Integrity. CoRR (2024)
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)
Rachel Cummings, Hadi Elzayn, Emmanouil Pountourakis, Vasilis Gkatzelis, Juba Ziani
Optimal Data Acquisition with Privacy-Aware Agents. SaTML (2023)
Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar
Mean Estimation with User-level Privacy under Data Heterogeneity. CoRR (2023)
Saeyoung Rho, Rachel Cummings, Vishal Misra
Differentially Private Synthetic Control. AISTATS (2023)
Priyanka Nanayakkara, Mary Anne Smart, Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles
What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy. CoRR (2023)
Rachel Cummings, Jayshree Sarathy
Centering Policy and Practice: Research Gaps Around Usable Differential Privacy. TPS-ISA (2023)
Saeyoung Rho, Rachel Cummings, Vishal Misra
Differentially Private Synthetic Control. CoRR (2023)
Sloan Nietert, Rachel Cummings, Ziv Goldfeld
Robust Estimation under the Wasserstein Distance. CoRR (2023)
Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub
An active learning framework for multi-group mean estimation. NeurIPS (2023)
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)
Inbal Dekel, Rachel Cummings, Ori Heffetz, Katrina Ligett
The Privacy Elasticity of Behavior: Conceptualization and Application. EC (2023)
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 (2023)
Sloan Nietert, Ziv Goldfeld, Rachel Cummings
Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis. AISTATS (2022)
Wanrong Zhang, Yajun Mei, Rachel Cummings
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size. AISTATS (2022)
Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar
Mean Estimation with User-level Privacy under Data Heterogeneity. NeurIPS (2022)
Rachel Cummings, Hadi Elzayn, Vasilis Gkatzelis, Emmanouil Pountourakis, Juba Ziani
Optimal Data Acquisition with Privacy-Aware Agents. CoRR (2022)
Wanrong Zhang, Olga Ohrimenko, Rachel Cummings
Attribute Privacy: Framework and Mechanisms. FAccT (2022)
Wanrong Zhang, Yajun Mei, Rachel Cummings
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size. CoRR (2022)
Qiaomei Li, Rachel Cummings, Yonatan Mintz
Optimal Local Explainer Aggregation for Interpretable Prediction. AAAI (2022)
Wanrong Zhang, Gautam Kamath, Rachel Cummings
PAPRIKA: Private Online False Discovery Rate Control. ICML (2021)
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) (2021)
Sebastian Perez-Salazar, Rachel Cummings
Differentially Private Online Submodular Maximization. AISTATS (2021)
Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles
"I need a better description": An Investigation Into User Expectations For Differential Privacy. CCS (2021)
Sloan Nietert, Rachel Cummings, Ziv Goldfeld
Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis. CoRR (2021)
Chris Waites, Rachel Cummings
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation. AIES (2021)
Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings
Single and Multiple Change-Point Detection with Differential Privacy. J. Mach. Learn. Res. 22 (2021)
Yuliia Lut, Michael Wang, Elissa M. Redmiles, Rachel Cummings
How we browse: Measurement and analysis of digital behavior. CoRR (2021)
Uthaipon Tao Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, Rachel Cummings
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning. IISA (2021)
Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles
"I need a better description": An Investigation Into User Expectations For Differential Privacy. CoRR (2021)
Chris Waites, Rachel Cummings
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation. CoRR (2021)
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) (2021)
Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang
Privately detecting changes in unknown distributions. ICML (2020)
Qiaomei Li, Rachel Cummings, Yonatan Mintz
Locally Interpretable Predictions of Parkinson's Disease Progression. CoRR (2020)
Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan
The Possibilities and Limitations of Private Prediction Markets. ACM Trans. Economics and Comput. 8 (3) (2020)
Rachel Cummings, Michael Hay
TPDP'20: 6th Workshop on Theory and Practice of Differential Privacy. CCS (2020)
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 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) (2020)
Sebastian Perez-Salazar, Rachel Cummings
Differentially Private Online Submodular Maximization. CoRR (2020)
Wanrong Zhang, Olga Ohrimenko, Rachel Cummings
Attribute Privacy: Framework and Mechanisms. CoRR (2020)
Rachel Cummings, Nikhil R. Devanur, Zhiyi Huang, Xiangning Wang
Algorithmic Price Discrimination. SODA (2020)
Rachel Cummings, David Durfee
Individual Sensitivity Preprocessing for Data Privacy. SODA (2020)
Wanrong Zhang, Gautam Kamath, Rachel Cummings
PAPRIKA: Private Online False Discovery Rate Control. CoRR (2020)
Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang
Privately detecting changes in unknown distributions. CoRR (2019)
Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie Morgenstern
Learning Auctions with Robust Incentive Guarantees. NeurIPS (2019)
Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, Rachel Cummings
Differentially Private Mixed-Type Data Generation For Unsupervised Learning. CoRR (2019)
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 (2019)
Adrian Rivera Cardoso, Rachel Cummings
Differentially Private Online Submodular Minimization. AISTATS (2019)
Rachel Cummings, Varun Gupta, Dhamma Kimpara, Jamie Morgenstern
On the Compatibility of Privacy and Fairness. UMAP (Adjunct Publication) (2019)
Rachel Cummings, Nikhil R. Devanur, Zhiyi Huang, Xiangning Wang
Algorithmic Price Discrimination. CoRR (2019)
Adrian Rivera Cardoso, Rachel Cummings
Differentially Private Online Submodular Optimization. CoRR (2018)
Rachel Cummings, Sara Krehbiel, Kevin A. Lai, Uthaipon Tao Tantipongpipat
Differential Privacy for Growing Databases. NeurIPS (2018)
Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang
Differentially Private Change-Point Detection. CoRR (2018)
Rachel Cummings, David Durfee
Individual Sensitivity Preprocessing for Data Privacy. CoRR (2018)
Rachel Cummings, Sara Krehbiel, Kevin A. Lai, Uthaipon Tantipongpipat
Differential Privacy for Growing Databases. CoRR (2018)
Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang
Differentially Private Change-Point Detection. NeurIPS (2018)
Rachel Cummings
Differential privacy as a tool for truthfulness in games. XRDS 24 (1) (2017)
Ho-Lin Chen, Rachel Cummings, David Doty, David Soloveichik
Speed faults in computation by chemical reaction networks. Distributed Comput. 30 (5) (2017)
Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu
Coordination Complexity: Small Information Coordinating Large Populations. ITCS (2016)
Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan
The Possibilities and Limitations of Private Prediction Markets. CoRR (2016)
Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu
Adaptive Learning with Robust Generalization Guarantees. COLT (2016)
Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan
The Possibilities and Limitations of Private Prediction Markets. EC (2016)
Rachel Cummings, David Doty, David Soloveichik
Probability 1 computation with chemical reaction networks. Nat. Comput. 15 (2) (2016)
Rachel Cummings, Katrina Ligett, Mallesh M. Pai, Aaron Roth
The Strange Case of Privacy in Equilibrium Models. EC (2016)
Rachel Cummings, Federico Echenique, Adam Wierman
The Empirical Implications of Privacy-Aware Choice. Oper. Res. 64 (1) (2016)
Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu
Adaptive Learning with Robust Generalization Guarantees. CoRR (2016)
Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth
Online Learning and Profit Maximization from Revealed Preferences. AAAI (2015)
Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
Privacy and Truthful Equilibrium Selection for Aggregative Games. WINE (2015)
Rachel Cummings, Stratis Ioannidis, Katrina Ligett
Truthful Linear Regression. CoRR (2015)
Rachel Cummings, Katrina Ligett, Mallesh M. Pai, Aaron Roth
The Strange Case of Privacy in Equilibrium Models. CoRR (2015)
Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, Juba Ziani
Accuracy for Sale: Aggregating Data with a Variance Constraint. ITCS (2015)
Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu
Coordination Complexity: Small Information Coordinating Large Populations. CoRR (2015)
Rachel Cummings, Stratis Ioannidis, Katrina Ligett
Truthful Linear Regression. COLT (2015)
Ho-Lin Chen, Rachel Cummings, David Doty, David Soloveichik
Speed Faults in Computation by Chemical Reaction Networks. DISC (2014)
Rachel Cummings, Federico Echenique, Adam Wierman
The Empirical Implications of Privacy-Aware Choice. CoRR (2014)
Rachel Cummings, David Doty, David Soloveichik
Probability 1 Computation with Chemical Reaction Networks. DNA (2014)
Rachel Cummings, Federico Echenique, Adam Wierman
The empirical implications of privacy-aware choice. EC (2014)
Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth
Online Learning and Profit Maximization from Revealed Preferences. CoRR (2014)
Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
Privacy and Truthful Equilibrium Selection for Aggregative Games. CoRR (2014)
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)