Ira Globus-Harris

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Michael KearnsProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Aaron RothProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Lucy QinGeorgetown UniversityVerified email at georgetown.edu
Kinan Dak AlbabPhD Student, Brown UniversityVerified email at brown.edu
Declan HarrisonStudent, University of PennsylvaniaVerified email at seas.upenn.edu
Adam SmithProfessor of Computer Science, Boston UniversityVerified email at bu.edu
Audra McMillanAppleVerified email at apple.com
Varun GuptaMeta - Modeling & OptimizationVerified email at fb.com
Jessica SorrellJohns Hopkins UniversityVerified email at jhu.edu
Joerg DrechslerInstitut für Arbeitsmarkt- und Berufsforschung, University of MarylandVerified email at iab.de
Adam GroceReed CollegeVerified email at reed.edu
Marika SwanbergGoogleVerified email at google.com
Jamie MorgensternUniversity of WashingtonVerified email at cs.washington.edu
Christopher JungMetaVerified email at stanford.edu
Emily DianaTepper School of Business, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Saeed Sharifi-MalvajerdiResearch Assistant Professor, Toyota Technological Institute at Chicago (TTIC)Verified email at ttic.edu
Rawane IssaCryptography & Secure Computation Researcher at Galois, Inc.Verified email at bu.edu
Pietro PeronaCalifornia Institute of TechnologyVerified email at caltech.edu

Ira Globus-Harris

Graduate Student, University of Pennsylvania

Verified email at seas.upenn.edu - Homepage

algorithmic fairness differential privacy learning theory


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From usability to secure computing and back again L Qin, A Lapets, F Jansen, P Flockhart, KD Albab, I Globus-Harris, ... Fifteenth Symposium on Usable Privacy and Security ({SOUPS} 2019), 191-210, 2019	33	2019
Multicalibration as Boosting for Regression I Globus-Harris, D Harrison, M Kearns, A Roth, J Sorrell arXiv preprint arXiv:2301.13767, 2023	29	2023
An algorithmic framework for bias bounties I Globus-Harris, M Kearns, A Roth 2022 ACM Conference on Fairness, Accountability, and Transparency, 1106-1124, 2022	28	2022
Nonparametric differentially private confidence intervals for the median J Drechsler, I Globus-Harris, A McMillan, J Sarathy, A Smith Journal of Survey Statistics and Methodology 10 (3), 804-829, 2022	24	2022
Improved differentially private analysis of variance M Swanberg, I Globus-Harris, I Griffith, A Ritz, A Groce, A Bray Proceedings on Privacy Enhancing Technologies 2019 (3), 2019	22	2019
Multicalibrated regression for downstream fairness I Globus-Harris, V Gupta, C Jung, M Kearns, J Morgenstern, A Roth Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 259-286, 2023	13	2023
Lexicographically Fair Learning: Algorithms and Generalization E Diana, W Gill, I Globus-Harris, M Kearns, A Roth, S Sharifi-Malvajerdi arXiv preprint arXiv:2102.08454, 2021	10	2021
Beyond the Frontier: Fairness Without Accuracy Loss I Globus-Harris, M Kearns, A Roth arXiv e-prints, arXiv: 2201.10408, 2022	8	2022
Tutorial: Deploying Secure Multi-Party Computation on the Web Using JIFF KD Albab, R Issa, A Lapets, P Flockhart, L Qin, I Globus-Harris 2019 IEEE Cybersecurity Development (SecDev), 3-3, 2019	8	2019
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning I Globus-Harris, D Harrison, M Kearns, P Perona, A Roth The 2024 ACM Conference on Fairness, Accountability, and Transparency, 529-545, 2024		2024
Model Ensembling for Constrained Optimization I Globus-Harris, V Gupta, M Kearns, A Roth arXiv preprint arXiv:2405.16752, 2024		2024
Non-parametric differentially private confidence intervals for the median A Smith, J Drechsler, I Globus-Harris, A McMillan, J Sarathy https://arxiv. org/abs/2106.10333, 2021		2021

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