Rachel Cummings

According to our database, Rachel Cummings authored at least 49 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Integrating Differential Privacy and Contextual Integrity.

Sebastian Benthall, Rachel Cummings

Proceedings of the Symposium on Computer Science and Law, 2024

2023

"I need a better description": An Investigation Into User Expectations For Differential Privacy.

Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles

J. Priv. Confidentiality, August, 2023

Challenges towards the Next Frontier in Privacy.

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

CoRR, 2023

Robust Estimation under the Wasserstein Distance.

Sloan Nietert, Rachel Cummings, Ziv Goldfeld

CoRR, 2023

What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy.

Priyanka Nanayakkara, Mary Anne Smart, Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles

Proceedings of the 32nd USENIX Security Symposium, 2023

Centering Policy and Practice: Research Gaps Around Usable Differential Privacy.

Rachel Cummings, Jayshree Sarathy

Proceedings of the 5th IEEE International Conference on Trust, 2023

The Privacy Elasticity of Behavior: Conceptualization and Application.

Inbal Dekel, Rachel Cummings, Ori Heffetz, Katrina Ligett

Proceedings of the 24th ACM Conference on Economics and Computation, 2023

An active learning framework for multi-group mean estimation.

Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Differentially Private Synthetic Control.

Saeyoung Rho, Rachel Cummings, Vishal Misra

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Optimal Data Acquisition with Privacy-Aware Agents.

Rachel Cummings, Hadi Elzayn, Vasilis Gkatzelis, Emmanouil Pountourakis, Juba Ziani

CoRR, 2022

Mean Estimation with User-level Privacy under Data Heterogeneity.

Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Attribute Privacy: Framework and Mechanisms.

Wanrong Zhang, Olga Ohrimenko, Rachel Cummings

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis.

Sloan Nietert, Ziv Goldfeld, Rachel Cummings

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size.

Wanrong Zhang, Yajun Mei, Rachel Cummings

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Optimal Local Explainer Aggregation for Interpretable Prediction.

Qiaomei Li, Rachel Cummings, Yonatan Mintz

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Single and Multiple Change-Point Detection with Differential Privacy.

Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings

J. Mach. Learn. Res., 2021

Differentially private synthetic mixed-type data generation for unsupervised learning.

Uthaipon Tao Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, Rachel Cummings

Intell. Decis. Technol., 2021

Advances and Open Problems in Federated Learning.

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

Found. Trends Mach. Learn., 2021

How we browse: Measurement and analysis of digital behavior.

Yuliia Lut, Michael Wang, Elissa M. Redmiles, Rachel Cummings

CoRR, 2021

PAPRIKA: Private Online False Discovery Rate Control.

Wanrong Zhang, Gautam Kamath, Rachel Cummings

Proceedings of the 38th International Conference on Machine Learning, 2021

Differentially Private Online Submodular Maximization.

Sebastian Perez-Salazar, Rachel Cummings

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation.

Chris Waites, Rachel Cummings

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020

The Possibilities and Limitations of Private Prediction Markets.

Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan

ACM Trans. Economics and Comput., 2020

Locally Interpretable Predictions of Parkinson's Disease Progression.

Qiaomei Li, Rachel Cummings, Yonatan Mintz

CoRR, 2020

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program.

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

AI Mag., 2020

Individual Sensitivity Preprocessing for Data Privacy.

Rachel Cummings, David Durfee

Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

Algorithmic Price Discrimination.

Rachel Cummings, Nikhil R. Devanur, Zhiyi Huang, Xiangning Wang

Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

Privately detecting changes in unknown distributions.

Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang

Proceedings of the 37th International Conference on Machine Learning, 2020

TPDP'20: 6th Workshop on Theory and Practice of Differential Privacy.

Rachel Cummings, Michael Hay

Proceedings of the CCS '20: 2020 ACM SIGSAC Conference on Computer and Communications Security, 2020

2019

Advances and Open Problems in Federated Learning.

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

CoRR, 2019

Differentially Private Mixed-Type Data Generation For Unsupervised Learning.

Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, Rachel Cummings

CoRR, 2019

On the Compatibility of Privacy and Fairness.

Rachel Cummings, Varun Gupta, Dhamma Kimpara, Jamie Morgenstern

Proceedings of the Adjunct Publication of the 27th Conference on User Modeling, 2019

Learning Auctions with Robust Incentive Guarantees.

Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie Morgenstern

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Differentially Private Online Submodular Minimization.

Adrian Rivera Cardoso, Rachel Cummings

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Differentially Private Online Submodular Optimization.

Adrian Rivera Cardoso, Rachel Cummings

CoRR, 2018

Differentially Private Change-Point Detection.

Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Differential Privacy for Growing Databases.

Rachel Cummings, Sara Krehbiel, Kevin A. Lai, Uthaipon Tao Tantipongpipat

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Speed faults in computation by chemical reaction networks.

Ho-Lin Chen, Rachel Cummings, David Doty, David Soloveichik

Distributed Comput., 2017

Differential privacy as a tool for truthfulness in games.

Rachel Cummings

XRDS, 2017

2016

Probability 1 computation with chemical reaction networks.

Rachel Cummings, David Doty, David Soloveichik

Nat. Comput., 2016

The Empirical Implications of Privacy-Aware Choice.

Rachel Cummings, Federico Echenique, Adam Wierman

Oper. Res., 2016

The Strange Case of Privacy in Equilibrium Models.

Rachel Cummings, Katrina Ligett, Mallesh M. Pai, Aaron Roth

Proceedings of the 2016 ACM Conference on Economics and Computation, 2016

Coordination Complexity: Small Information Coordinating Large Populations.

Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu

Proceedings of the 2016 ACM Conference on Innovations in Theoretical Computer Science, 2016

Adaptive Learning with Robust Generalization Guarantees.

Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu

Proceedings of the 29th Conference on Learning Theory, 2016

2015

Privacy and Truthful Equilibrium Selection for Aggregative Games.

Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu

Proceedings of the Web and Internet Economics - 11th International Conference, 2015

Accuracy for Sale: Aggregating Data with a Variance Constraint.

Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, Juba Ziani

Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science, 2015

Truthful Linear Regression.

Rachel Cummings, Stratis Ioannidis, Katrina Ligett

Proceedings of The 28th Conference on Learning Theory, 2015

Online Learning and Profit Maximization from Revealed Preferences.

Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2011

Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate.

Wei Chen, Alex Collins, Rachel Cummings, Te Ke, Zhenming Liu, David Rincón, Xiaorui Sun, Yajun Wang, Wei Wei, Yifei Yuan

Proceedings of the Eleventh SIAM International Conference on Data Mining, 2011