Rachel Cummings

98 papers A* 23A 11B 1C 1Journal 54Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	"Having Confidence in My Confidence Intervals": How Data Users Engage with Privacy-Protected Wikipedia Data. CHI Harold Triedman, Jayshree Sarathy, Priyanka Nanayakkara, Rachel Cummings, Gabriel Kaptchuk, Sean Kross, Elissa M. Redmiles
2025	J	`jnl`	"Having Confidence in My Confidence Intervals": How Data Users Engage with Privacy-Protected Wikipedia Data. CoRR Harold Triedman, Jayshree Sarathy, Priyanka Nanayakkara, Rachel Cummings, Gabriel Kaptchuk, Sean Kross, Elissa M. Redmiles
2025	J	`jnl`	An active learning framework for multi-group mean estimation. CoRR Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub
2025	J	`jnl`	Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning. CoRR Roy Rinberg, Ilia Shumailov, Vikrant Singhal, Rachel Cummings, Nicolas Papernot
2025	A	`conf`	ClusterSC: Advancing Synthetic Control with Donor Selection. AISTATS Saeyoung Rho, Andrew Tang, Noah Bergam, Rachel Cummings, Vishal Misra
2025	J	`jnl`	ClusterSC: Advancing Synthetic Control with Donor Selection. CoRR Saeyoung Rho, Andrew Tang, Noah Bergam, Rachel Cummings, Vishal Misra
2025	A*	`conf`	Differential Privacy Under Class Imbalance: Methods and Empirical Insights. ICML Lucas Rosenblatt, Yuliia Lut, Ethan Turok, Marco Avella Medina, Rachel Cummings
2025	A*	`conf`	Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model. ICML Rachel Cummings, Alessandro Epasto, Jieming Mao, Tamalika Mukherjee, Tingting Ou, Peilin Zhong
2025	J	`jnl`	Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model. CoRR Rachel Cummings, Alessandro Epasto, Jieming Mao, Tamalika Mukherjee, Tingting Ou, Peilin Zhong
2025	J	`jnl`	Models Matter: Setting Accurate Privacy Expectations for Local and Central Differential Privacy. Proc. Priv. Enhancing Technol. Mary Anne Smart, Priyanka Nanayakkara, Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles
2024	J	`jnl`	ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries. CoRR Rachel Cummings, Shlomi Hod, Jayshree Sarathy, Marika Swanberg
2024	J	`jnl`	Centering Policy and Practice: Research Gaps around Usable Differential Privacy. CoRR Rachel Cummings, Jayshree Sarathy
2024	J	`jnl`	Differential Privacy Under Class Imbalance: Methods and Empirical Insights. CoRR Lucas Rosenblatt, Yuliia Lut, Eitan Turok, Marco Avella-Medina, Rachel Cummings
2024	J	`jnl`	Differentially Private Synthetic Control. J. Priv. Confidentiality Saeyoung Rho, Rachel Cummings, Vishal Misra
2024	—	`conf`	How We Browse: Measurement and Analysis of Browsing Behavior. CogMI Yuliia Lut, Michael Wang, Elissa M. Redmiles, Rachel Cummings
2024	—	`conf`	Integrating Differential Privacy and Contextual Integrity. CSLAW Sebastian Benthall, Rachel Cummings
2024	J	`jnl`	Integrating Differential Privacy and Contextual Integrity. CoRR Sebastian Benthall, Rachel Cummings
2024	J	`jnl`	Models Matter: Setting Accurate Privacy Expectations for Local and Central Differential Privacy. CoRR Mary Anne Smart, Priyanka Nanayakkara, Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles
2024	A	`conf`	Thompson Sampling Itself is Differentially Private. AISTATS Tingting Ou, Rachel Cummings, Marco Avella Medina
2024	J	`jnl`	Thompson Sampling Itself is Differentially Private. CoRR Tingting Ou, Marco Avella Medina, Rachel Cummings
2023	J	`jnl`	"I need a better description": An Investigation Into User Expectations For Differential Privacy. J. Priv. Confidentiality Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles
2023	A*	`conf`	An active learning framework for multi-group mean estimation. NeurIPS Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub
2023	—	`conf`	Centering Policy and Practice: Research Gaps Around Usable Differential Privacy. TPS-ISA Rachel Cummings, Jayshree Sarathy
2023	J	`jnl`	Challenges towards the Next Frontier in Privacy. CoRR 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
2023	A	`conf`	Differentially Private Synthetic Control. AISTATS Saeyoung Rho, Rachel Cummings, Vishal Misra
2023	J	`jnl`	Differentially Private Synthetic Control. CoRR Saeyoung Rho, Rachel Cummings, Vishal Misra
2023	J	`jnl`	Mean Estimation with User-level Privacy under Data Heterogeneity. CoRR Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar
2023	—	`conf`	Optimal Data Acquisition with Privacy-Aware Agents. SaTML Rachel Cummings, Hadi Elzayn, Emmanouil Pountourakis, Vasilis Gkatzelis, Juba Ziani
2023	J	`jnl`	Robust Estimation under the Wasserstein Distance. CoRR Sloan Nietert, Rachel Cummings, Ziv Goldfeld
2023	A*	`conf`	The Privacy Elasticity of Behavior: Conceptualization and Application. EC Inbal Dekel, Rachel Cummings, Ori Heffetz, Katrina Ligett
2023	A*	`conf`	What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy. USENIX Security Symposium Priyanka Nanayakkara, Mary Anne Smart, Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles
2023	J	`jnl`	What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy. CoRR Priyanka Nanayakkara, Mary Anne Smart, Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles
2022	—	`conf`	Attribute Privacy: Framework and Mechanisms. FAccT Wanrong Zhang, Olga Ohrimenko, Rachel Cummings
2022	A*	`conf`	Mean Estimation with User-level Privacy under Data Heterogeneity. NeurIPS Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar
2022	J	`jnl`	Optimal Data Acquisition with Privacy-Aware Agents. CoRR Rachel Cummings, Hadi Elzayn, Vasilis Gkatzelis, Emmanouil Pountourakis, Juba Ziani
2022	A*	`conf`	Optimal Local Explainer Aggregation for Interpretable Prediction. AAAI Qiaomei Li, Rachel Cummings, Yonatan Mintz
2022	A	`conf`	Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis. AISTATS Sloan Nietert, Ziv Goldfeld, Rachel Cummings
2022	A	`conf`	Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size. AISTATS Wanrong Zhang, Yajun Mei, Rachel Cummings
2022	J	`jnl`	Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size. CoRR Wanrong Zhang, Yajun Mei, Rachel Cummings
2021	A*	`conf`	"I need a better description": An Investigation Into User Expectations For Differential Privacy. CCS Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles
2021	J	`jnl`	"I need a better description": An Investigation Into User Expectations For Differential Privacy. CoRR Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles
2021	J	`jnl`	Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 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
2021	C	`conf`	Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation. AIES Chris Waites, Rachel Cummings
2021	J	`jnl`	Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation. CoRR Chris Waites, Rachel Cummings
2021	A	`conf`	Differentially Private Online Submodular Maximization. AISTATS Sebastian Perez-Salazar, Rachel Cummings
2021	—	`conf`	Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning. IISA Uthaipon Tao Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, Rachel Cummings
2021	J	`jnl`	Differentially private synthetic mixed-type data generation for unsupervised learning. Intell. Decis. Technol. Uthaipon Tao Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, Rachel Cummings
2021	J	`jnl`	How we browse: Measurement and analysis of digital behavior. CoRR Yuliia Lut, Michael Wang, Elissa M. Redmiles, Rachel Cummings
2021	J	`jnl`	Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis. CoRR Sloan Nietert, Rachel Cummings, Ziv Goldfeld
2021	A*	`conf`	PAPRIKA: Private Online False Discovery Rate Control. ICML Wanrong Zhang, Gautam Kamath, Rachel Cummings
2021	J	`jnl`	Single and Multiple Change-Point Detection with Differential Privacy. J. Mach. Learn. Res. Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings
2020	A*	`conf`	Algorithmic Price Discrimination. SODA Rachel Cummings, Nikhil R. Devanur, Zhiyi Huang, Xiangning Wang
2020	J	`jnl`	Attribute Privacy: Framework and Mechanisms. CoRR Wanrong Zhang, Olga Ohrimenko, Rachel Cummings
2020	J	`jnl`	Differentially Private Online Submodular Maximization. CoRR Sebastian Perez-Salazar, Rachel Cummings
2020	A*	`conf`	Individual Sensitivity Preprocessing for Data Privacy. SODA Rachel Cummings, David Durfee
2020	J	`jnl`	Locally Interpretable Predictions of Parkinson's Disease Progression. CoRR Qiaomei Li, Rachel Cummings, Yonatan Mintz
2020	J	`jnl`	PAPRIKA: Private Online False Discovery Rate Control. CoRR Wanrong Zhang, Gautam Kamath, Rachel Cummings
2020	A*	`conf`	Privately detecting changes in unknown distributions. ICML Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang
2020	A*	`conf`	TPDP'20: 6th Workshop on Theory and Practice of Differential Privacy. CCS Rachel Cummings, Michael Hay
2020	J	`jnl`	The Association for the Advancement of Artificial Intelligence 2020 Workshop Program. AI Mag. 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
2020	J	`jnl`	The Possibilities and Limitations of Private Prediction Markets. ACM Trans. Economics and Comput. Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan
2019	J	`jnl`	Advances and Open Problems in Federated Learning. CoRR 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
2019	J	`jnl`	Algorithmic Price Discrimination. CoRR Rachel Cummings, Nikhil R. Devanur, Zhiyi Huang, Xiangning Wang
2019	J	`jnl`	Differentially Private Mixed-Type Data Generation For Unsupervised Learning. CoRR Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, Rachel Cummings
2019	A	`conf`	Differentially Private Online Submodular Minimization. AISTATS Adrian Rivera Cardoso, Rachel Cummings
2019	A*	`conf`	Learning Auctions with Robust Incentive Guarantees. NeurIPS Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie Morgenstern
2019	—	`conf`	On the Compatibility of Privacy and Fairness. UMAP (Adjunct Publication) Rachel Cummings, Varun Gupta, Dhamma Kimpara, Jamie Morgenstern
2019	J	`jnl`	Privately detecting changes in unknown distributions. CoRR Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang
2018	A*	`conf`	Differential Privacy for Growing Databases. NeurIPS Rachel Cummings, Sara Krehbiel, Kevin A. Lai, Uthaipon Tao Tantipongpipat
2018	J	`jnl`	Differential Privacy for Growing Databases. CoRR Rachel Cummings, Sara Krehbiel, Kevin A. Lai, Uthaipon Tantipongpipat
2018	A*	`conf`	Differentially Private Change-Point Detection. NeurIPS Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang
2018	J	`jnl`	Differentially Private Change-Point Detection. CoRR Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang
2018	J	`jnl`	Differentially Private Online Submodular Optimization. CoRR Adrian Rivera Cardoso, Rachel Cummings
2018	J	`jnl`	Individual Sensitivity Preprocessing for Data Privacy. CoRR Rachel Cummings, David Durfee
2017	J	`jnl`	Differential privacy as a tool for truthfulness in games. XRDS Rachel Cummings
2017	J	`jnl`	Speed faults in computation by chemical reaction networks. Distributed Comput. Ho-Lin Chen, Rachel Cummings, David Doty, David Soloveichik
2016	A*	`conf`	Adaptive Learning with Robust Generalization Guarantees. COLT Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu
2016	J	`jnl`	Adaptive Learning with Robust Generalization Guarantees. CoRR Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu
2016	A	`conf`	Coordination Complexity: Small Information Coordinating Large Populations. ITCS Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu
2016	J	`jnl`	Probability 1 computation with chemical reaction networks. Nat. Comput. Rachel Cummings, David Doty, David Soloveichik
2016	J	`jnl`	The Empirical Implications of Privacy-Aware Choice. Oper. Res. Rachel Cummings, Federico Echenique, Adam Wierman
2016	A*	`conf`	The Possibilities and Limitations of Private Prediction Markets. EC Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan
2016	J	`jnl`	The Possibilities and Limitations of Private Prediction Markets. CoRR Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan
2016	A*	`conf`	The Strange Case of Privacy in Equilibrium Models. EC Rachel Cummings, Katrina Ligett, Mallesh M. Pai, Aaron Roth
2015	A	`conf`	Accuracy for Sale: Aggregating Data with a Variance Constraint. ITCS Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, Juba Ziani
2015	J	`jnl`	Coordination Complexity: Small Information Coordinating Large Populations. CoRR Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu
2015	A*	`conf`	Online Learning and Profit Maximization from Revealed Preferences. AAAI Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth
2015	—	`conf`	Privacy and Truthful Equilibrium Selection for Aggregative Games. WINE Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
2015	J	`jnl`	The Strange Case of Privacy in Equilibrium Models. CoRR Rachel Cummings, Katrina Ligett, Mallesh M. Pai, Aaron Roth
2015	A*	`conf`	Truthful Linear Regression. COLT Rachel Cummings, Stratis Ioannidis, Katrina Ligett
2015	J	`jnl`	Truthful Linear Regression. CoRR Rachel Cummings, Stratis Ioannidis, Katrina Ligett
2014	J	`jnl`	Online Learning and Profit Maximization from Revealed Preferences. CoRR Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth
2014	J	`jnl`	Privacy and Truthful Equilibrium Selection for Aggregative Games. CoRR Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
2014	B	`conf`	Probability 1 Computation with Chemical Reaction Networks. DNA Rachel Cummings, David Doty, David Soloveichik
2014	A	`conf`	Speed Faults in Computation by Chemical Reaction Networks. DISC Ho-Lin Chen, Rachel Cummings, David Doty, David Soloveichik
2014	J	`jnl`	The Empirical Implications of Privacy-Aware Choice. CoRR Rachel Cummings, Federico Echenique, Adam Wierman
2014	A*	`conf`	The empirical implications of privacy-aware choice. EC Rachel Cummings, Federico Echenique, Adam Wierman
2011	A	`conf`	Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate. SDM Wei Chen, Alex Collins, Rachel Cummings, Te Ke, Zhenming Liu, David Rincón, Xiaorui Sun, Yajun Wang, Wei Wei, Yifei Yuan