Ligett, Katrina

14 publications

NeurIPS 2025 How Well Can Differential Privacy Be Audited in One Run? Amit Keinan, Moshe Shenfeld, Katrina Ligett

NeurIPS 2025 The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches Omri Lev, Vishwak Srinivasan, Moshe Shenfeld, Katrina Ligett, Ayush Sekhari, Ashia C. Wilson

NeurIPS 2023 Generalization in the Face of Adaptivity: A Bayesian Perspective Moshe Shenfeld, Katrina Ligett

AISTATS 2021 Gaming Helps! Learning from Strategic Interactions in Natural Dynamics Yahav Bechavod, Katrina Ligett, Steven Wu, Juba Ziani

AISTATS 2021 Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization Vikas Garg, Adam Tauman Kalai, Katrina Ligett, Steven Wu

ALT 2021 Algorithmic Learning Theory 2021: Preface Vitaly Feldman, Katrina Ligett, Sivan Sabato

COLT 2020 Privately Learning Thresholds: Closing the Exponential Gap Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer

NeurIPS 2019 A Necessary and Sufficient Stability Notion for Adaptive Generalization Moshe Shenfeld, Katrina Ligett

NeurIPS 2019 Equal Opportunity in Online Classification with Partial Feedback Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Steven Z. Wu

NeurIPS 2017 Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Steven Z. Wu

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

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

COLT 2013 A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and Regret Lachlan L. H. Andrew, Siddharth Barman, Katrina Ligett, Minghong Lin, Adam Meyerson, Alan Roytman, Adam Wierman

NeurIPS 2012 A Simple and Practical Algorithm for Differentially Private Data Release Moritz Hardt, Katrina Ligett, Frank Mcsherry