Cummings, Rachel

23 publications

AISTATS 2025 ClusterSC: Advancing Synthetic Control with Donor Selection Saeyoung Rho, Andrew Tang, Noah Bergam, Rachel Cummings, Vishal Misra

ICML 2025 Differential Privacy Under Class Imbalance: Methods and Empirical Insights Lucas Rosenblatt, Yuliia Lut, Ethan Turok, Marco Avella Medina, Rachel Cummings

ICML 2025 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

AISTATS 2024 Thompson Sampling Itself Is Differentially Private Tingting Ou, Rachel Cummings, Marco Avella Medina

NeurIPS 2023 An Active Learning Framework for Multi-Group Mean Estimation Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub

AISTATS 2023 Differentially Private Synthetic Control Saeyoung Rho, Rachel Cummings, Vishal Misra

AISTATS 2022 Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis Sloan Nietert, Ziv Goldfeld, Rachel Cummings

AISTATS 2022 Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size Wanrong Zhang, Yajun Mei, Rachel Cummings

NeurIPS 2022 Mean Estimation with User-Level Privacy Under Data Heterogeneity Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar

AAAI 2022 Optimal Local Explainer Aggregation for Interpretable Prediction Qiaomei Li, Rachel Cummings, Yonatan Mintz

AISTATS 2021 Differentially Private Online Submodular Maximization Sebastian Perez Salazar, Rachel Cummings

FnTML 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

NeurIPSW 2021 Mean Estimation with User-Level Privacy Under Data Heterogeneity Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar

ICML 2021 PAPRIKA: Private Online False Discovery Rate Control Wanrong Zhang, Gautam Kamath, Rachel Cummings

JMLR 2021 Single and Multiple Change-Point Detection with Differential Privacy Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings

ICML 2020 Privately Detecting Changes in Unknown Distributions Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang

AISTATS 2019 Differentially Private Online Submodular Minimization Adrian Rivera Cardoso, Rachel Cummings

NeurIPS 2019 Learning Auctions with Robust Incentive Guarantees Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie H Morgenstern

NeurIPS 2018 Differential Privacy for Growing Databases Rachel Cummings, Sara Krehbiel, Kevin A Lai, Uthaipon Tantipongpipat

NeurIPS 2018 Differentially Private Change-Point Detection Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang

COLT 2016 Adaptive Learning with Robust Generalization Guarantees Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu

AAAI 2015 Online Learning and Profit Maximization from Revealed Preferences Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth

COLT 2015 Truthful Linear Regression Rachel Cummings, Stratis Ioannidis, Katrina Ligett