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- What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy. Priyanka Nanayakkara, Mary Anne Smart, Rachel Cummings, Gabriel Kaptchuk, Elissa Redmiles. Submitted, 2023.
- Robust Estimation under the Wasserstein Distance. Sloan Nietert, Rachel Cummings, and Ziv Goldfeld. Submitted, 2023.
- The Privacy Elasticity of Behavior: Conceptualization and Application. Inbal Dekel, Rachel Cummings, Ori Heffetz, and Katrina Ligett. Submitted, 2022.
- Integrating Contextual Integrity and Differential Privacy. Sebastian Benthall and Rachel Cummings. Preliminary draft appeared at PLSC 2022.
- Differentially Private Synthetic Control. Saeyoung Rho, Rachel Cummings, and Vishal Misra. AISTATS, 2023.
- Optimal Data Acquisition with Privacy-Aware Agents. Rachel Cummings, Hadi Elzayn, Vasilis Gkatzelis, Emmanouil Pountourakis, Juba Ziani. SaTML, 2023. Best Paper Award
- Multi-Layer Influence Network Modeling of Critical Infrastructure Interdependencies. Zixing Wang, Rachel Cummings, and Chelsea C. White III. Submitted, 2022.
- Mean Estimation with User-Level Privacy under Data Heterogeneity. Rachel Cummings, Vitaly Feldman, Audra McMillan, and Kunal Talwar. NeurIPS, 2022.
- Private Sequential Hypothesis Testing for Statisticians: Privacy, Error, and Sample Size. Wanrong Zhang, Yajun Mei, and Rachel Cummings. AISTATS, 2022.
- Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis. Sloan Nietert, Rachel Cummings, and Ziv Goldfeld. AISTATS, 2022.
- How we browse: Measurement and analysis of digital behavior. Yuliia Lut, Michael Wang, Elissa Redmiles, and Rachel Cummings. Submitted, 2022.
- Attribute Privacy: Framework and Mechanisms. Wanrong Zhang, Olga Ohrimenko, and Rachel Cummings. FAccT 2022.
- Optimal Local Explainer Aggregation for Interpretable Prediction. Qiaomei Li, Rachel Cummings, and Yonatan Mintz. AAAI 2022. (Preliminary version was titled “Locally Interpretable Predictions of Parkinson’s Disease Progression”.)
- “I need a better description”: An Investigation Into User Expectations For Differential Privacy. Rachel Cummings, Gabriel Kaptchuk, and Elissa Redmiles. CCS 2021. Best Paper Award, Runner Up. Journal version: Journal of Privacy and Confidentiality, R&R 2022.
- Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation. Chris Waits and Rachel Cummings. AIES 2021.
- Differentially Private Online Submodular Maximization. Sebastian Perez-Salazar and Rachel Cummings. AISTATS 2021.
- PAPRIKA: Private Online False Discovery Rate Control. Wanrong Zhang, Gautam Kamath, and Rachel Cummings. ICML 2021.
- Differentially Private Synthetic Mixed-Type Data Generation for Unsupervised Learning. Uthaipon (Tao) Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh (Ankit) Siva, and Rachel Cummings. IISA 2021. Journal version: Intelligent Decision Technologies, 2022.
- Privately Detecting Changes in Unknown Distributions. Rachel Cummings, Sara Krehbiel, Yuliia Lut, and Wanrong Zhang. ICML 2020.
- Single and Multiple Change-Point Detection with Differential Privacy. Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, and Rachel Cummings. JMLR, 2021. (Preliminary version appeared at NeurIPS 2018)
- Algorithmic Price Discrimination. Rachel Cummings, Nikhil Devanur, Zhiyi Huang, and Xiangning Wang. SODA 2020.
- Individual Sensitivity Preprocessing for Data Privacy. Rachel Cummings and David Durfee. SODA 2020.
- Advances and Open Problems in Federated Learning. 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, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, 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. Foundations and Trends in Machine Learning, 2021
- Learning Auctions with Robust Incentive Guarantees. Jacob Abernethy, Rachel Cummings, Bhuvesh Kumar, Jamie Morgenstern, and Sam Taggart. NeurIPS 2019.
- On the Compatibility of Privacy and Fairness. Rachel Cummings, Varun Gupta, Dhamma Kimpara, and Jamie Morgenstern. FairUMAP 2019.
- Differentially Private Online Submodular Minimization. Adrian Rivera Cardoso and Rachel Cummings. AISTATS ’19.
- Private Synthetic Data Generation via GANs. Digvijay Boob, Rachel Cummings, Dhamma Kimpara, Uthaipon (Tao) Tantipongpipat, Chris Waites, and Kyle Zimmerman. NIST Unlinkable Data Challenge, 2018. Winner: Grand Prize; Winner: People’s Choice Award.
- The Role of Differential Privacy in GDPR Compliance. Rachel Cummings and Deven Desai. FATREC ’18.
- Differentially Private Change-Point Detection. Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, and Wanrong Zhang. NeurIPS ’18.
- Differential Privacy for Growing Databases. Rachel Cummings, Sara Krehbiel, Kevin Lai, and Uthaipon (Tao) Tantipongpipat. NeurIPS ’18.
- Differential Privacy as a Tool for Truthfulness in Games. Rachel Cummings. XRDS 2017.
- The Implications of Privacy-Aware Choice. Rachel Cummings. Ph.D. Thesis, Caltech 2017. Winner: Amori Doctoral Prize in Computing and Mathematical Sciences; Honorable Mention: SIGecom Doctoral Dissertation Award
- Adaptive Learning with Robust Generalization Guarantees. Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, and Zhiwei Steven Wu. COLT ’16.
- The Possibilities and Limitations of Private Prediction Markets. Rachel Cummings, David Pennock and Jennifer Wortman Vaughan. EC ’16. Journal version: TEAC (by invitation), 2021.
- The Strange Case of Privacy in Equilibrium Models. Rachel Cummings, Katrina Ligett, Mallesh Pai, and Aaron Roth. EC ’16.
- Coordination Complexity: Small Information Coordinating Large Populations. Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, and Zhiwei Steven Wu. ITCS ’16.
- Truthful Linear Regression. Rachel Cummings, Stratis Ioannidis and Katrina Ligett. COLT ’15.
- Accuracy for Sale: Aggregating Data with a Variance Constraint. Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, and Juba Ziani. ITCS ’15.
- Privacy and Truthful Equilibrium Selection for Aggregative Games. Rachel Cummings, Michael Kearns, Aaron Roth, and Zhiwei Steven Wu. WINE ’15.
- Online Learning and Profit Maximization from Revealed Preferences. Kareem Amin, Rachel Cummings, Lili Dworkin, Michael Kearns, and Aaron Roth. AAAI ’15.
- Probability 1 Computation with Chemical Reaction Networks. Rachel Cummings, Dave Doty and David Soloveichik. DNA ’14. Journal version: Natural Computing 2015.
- The Empirical Implications of Privacy-Aware Choice. Rachel Cummings, Federico Echenique and Adam Wierman. EC ’14. Journal version: Operations Research, 2016.
- Speed Faults in Computation by Chemical Reaction Networks. Ho-Lin Chen, Rachel Cummings, Dave Doty, and David Soloveichik. DISC ’14. Best paper award. Journal version: Distributed Computing, 2015.
- Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate. Wei Chen, Alex Collins, Rachel Cummings, Te Ke, Zhenming Liu, David Rincon, Xiaorui Sun, Yajun Wang, Wei Wei, and Yifei Yuan. SDM’11.