| 2026 | CHI | "Having Confidence in My Confidence Intervals": How Data Users Engage with Privacy-Protected Wikipedia Data. | Harold Triedman, Jayshree Sarathy, Priyanka Nanayakkara, Rachel Cummings, Gabriel Kaptchuk, Sean Kross, Elissa M. Redmiles |
| 2026 | COLT | A Complexity Measure for Active Learning in Multi-group Mean Estimation. | Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub |
| 2025 | AISTATS | ClusterSC: Advancing Synthetic Control with Donor Selection. | Saeyoung Rho, Andrew Tang, Noah Bergam, Rachel Cummings, Vishal Misra |
| 2025 | ICML | Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model. | Rachel Cummings, Alessandro Epasto, Jieming Mao, Tamalika Mukherjee, Tingting Ou, Peilin Zhong |
| 2025 | ICML | Differential Privacy Under Class Imbalance: Methods and Empirical Insights. | Lucas Rosenblatt, Yuliia Lut, Ethan Turok, Marco Avella Medina, Rachel Cummings |
| 2024 | AISTATS | Thompson Sampling Itself is Differentially Private. | Tingting Ou, Rachel Cummings, Marco Avella Medina |
| 2023 | AISTATS | Differentially Private Synthetic Control. | Saeyoung Rho, Rachel Cummings, Vishal Misra |
| 2022 | AAAI | Optimal Local Explainer Aggregation for Interpretable Prediction. | Qiaomei Li, Rachel Cummings, Yonatan Mintz |
| 2022 | AISTATS | Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size. | Wanrong Zhang, Yajun Mei, Rachel Cummings |
| 2022 | AISTATS | Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis. | Sloan Nietert, Ziv Goldfeld, Rachel Cummings |
| 2021 | AIES | Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation. | Chris Waites, Rachel Cummings |
| 2021 | AISTATS | Differentially Private Online Submodular Maximization. | Sebastian Perez-Salazar, Rachel Cummings |
| 2021 | CCS | "I need a better description": An Investigation Into User Expectations For Differential Privacy. | Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles |
| 2021 | ICML | PAPRIKA: Private Online False Discovery Rate Control. | Wanrong Zhang, Gautam Kamath, Rachel Cummings |
| 2020 | CCS | TPDP'20: 6th Workshop on Theory and Practice of Differential Privacy. | Rachel Cummings, Michael Hay |
| 2020 | ICML | Privately detecting changes in unknown distributions. | Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang |
| 2020 | SODA | Algorithmic Price Discrimination. | Rachel Cummings, Nikhil R. Devanur, Zhiyi Huang, Xiangning Wang |
| 2020 | SODA | Individual Sensitivity Preprocessing for Data Privacy. | Rachel Cummings, David Durfee |
| 2019 | AISTATS | Differentially Private Online Submodular Minimization. | Adrian Rivera Cardoso, Rachel Cummings |
| 2016 | COLT | Adaptive Learning with Robust Generalization Guarantees. | Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu |
| 2015 | AAAI | Online Learning and Profit Maximization from Revealed Preferences. | Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth |
| 2015 | COLT | Truthful Linear Regression. | Rachel Cummings, Stratis Ioannidis, Katrina Ligett |
| 2014 | DNA | Probability 1 Computation with Chemical Reaction Networks. | Rachel Cummings, David Doty, David Soloveichik |
| 2011 | SDM | Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate. | Wei Chen, Alex Collins, Rachel Cummings, Te Ke, Zhenming Liu, David Rincn, Xiaorui Sun, Yajun Wang, Wei Wei, Yifei Yuan |