Learning discrete distributions: user vs item-level privacy Y Liu, AT Suresh, FXX Yu, S Kumar, M Riley Advances in Neural Information Processing Systems 33, 20965-20976, 2020 | 57 | 2020 |
Interactive inference under information constraints J Acharya, CL Canonne, Y Liu, Z Sun, H Tyagi IEEE Transactions on Information Theory 68 (1), 502-516, 2021 | 45 | 2021 |
Estimating sparse discrete distributions under privacy and communication constraints J Acharya, P Kairouz, Y Liu, Z Sun Algorithmic Learning Theory, 79-98, 2021 | 18* | 2021 |
A perspective on data sharing in digital food safety systems C Qian, Y Liu, C Barnett-Neefs, S Salgia, O Serbetci, A Adalja, J Acharya, ... Critical Reviews in Food Science and Nutrition 63 (33), 12513-12529, 2023 | 17 | 2023 |
Discrete distribution estimation under user-level local differential privacy J Acharya, Y Liu, Z Sun International Conference on Artificial Intelligence and Statistics, 8561-8585, 2023 | 15 | 2023 |
Distributed estimation with multiple samples per user: Sharp rates and phase transition J Acharya, C Canonne, Y Liu, Z Sun, H Tyagi Advances in neural information processing systems 34, 18920-18931, 2021 | 8 | 2021 |
Algorithms for bounding contribution for histogram estimation under user-level privacy Y Liu, AT Suresh, W Zhu, P Kairouz, M Gruteser International Conference on Machine Learning, 21969-21996, 2023 | 5* | 2023 |
The role of randomness in quantum state certification with unentangled measurements Y Liu, J Acharya The Thirty Seventh Annual Conference on Learning Theory, 3523-3555, 2024 | | 2024 |
Self-supervised Learning for User Sequence Modeling Y Liu, L Ning, N Wu, K Singhal, PA Mansfield, D Berlowitz, S Prakash, ... | | |