Mao, Jieming

13 publications

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

ICML 2025 Retraining with Predicted Hard Labels Provably Increases Model Accuracy Rudrajit Das, Inderjit S Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong

NeurIPS 2024 Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo

NeurIPS 2024 Efficiency of the First-Price Auction in the Autobidding World Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo

ICLR 2022 Shuffle Private Stochastic Convex Optimization Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng

NeurIPS 2021 Robust Auction Design in the Auto-Bidding World Santiago Balseiro, Yuan Deng, Jieming Mao, Vahab Mirrokni, Song Zuo

COLT 2020 Pan-Private Uniformity Testing Kareem Amin, Matthew Joseph, Jieming Mao

NeurIPS 2020 Smoothly Bounding User Contributions in Differential Privacy Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Lijie Ren

ICML 2019 Differentially Private Fair Learning Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman

NeurIPS 2019 Locally Private Gaussian Estimation Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Steven Z. Wu

COLT 2019 Multi-Armed Bandit Problems with Strategic Arms Mark Braverman, Jieming Mao, Jon Schneider, S. Matthew Weinberg

COLT 2019 Sorted Top-K in Rounds Mark Braverman, Jieming Mao, Yuval Peres

NeurIPS 2018 Contextual Pricing for Lipschitz Buyers Jieming Mao, Renato Leme, Jon Schneider