| Isotropic hashing W Kong, WJ Li Advances in neural information processing systems 25, 2012 | 374 | 2012 |
| Efficient algorithms and lower bounds for robust linear regression I Diakonikolas, W Kong, A Stewart Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019 | 130 | 2019 |
| Manhattan hashing for large-scale image retrieval W Kong, WJ Li, M Guo Proceedings of the 35th international ACM SIGIR conference on Research and …, 2012 | 122 | 2012 |
| Double-bit quantization for hashing W Kong, WJ Li Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 634-640, 2012 | 106 | 2012 |
| Spectrum estimation from samples W Kong, G Valiant | 65 | 2017 |
| Spectre: Defending against backdoor attacks using robust covariance estimation J Hayase, W Kong International Conference on Machine Learning, 2020 | 62* | 2020 |
| Combining factorization model and additive forest for collaborative followee recommendation T Chen, L Tang, Q Liu, D Yang, S Xie, X Cao, C Wu, E Yao, Z Liu, Z Jiang, ... KDD CUP, 2012 | 60 | 2012 |
| Approximating the spectrum of a graph D Cohen-Steiner, W Kong, C Sohler, G Valiant Proceedings of the 24th acm sigkdd international conference on knowledge …, 2018 | 51 | 2018 |
| Meta-learning for mixed linear regression W Kong, R Somani, Z Song, S Kakade, S Oh International Conference on Machine Learning, 5394-5404, 2020 | 43 | 2020 |
| Robust and differentially private mean estimation X Liu, W Kong, S Kakade, S Oh Advances in neural information processing systems 34, 3887-3901, 2021 | 39 | 2021 |
| Maximum likelihood estimation for learning populations of parameters RK Vinayak, W Kong, G Valiant, S Kakade International Conference on Machine Learning, 6448-6457, 2019 | 39 | 2019 |
| Learning populations of parameters K Tian, W Kong, G Valiant Advances in neural information processing systems 30, 2017 | 39 | 2017 |
| Estimating learnability in the sublinear data regime W Kong, G Valiant Advances in Neural Information Processing Systems 31, 2018 | 32 | 2018 |
| Online model selection for reinforcement learning with function approximation J Lee, A Pacchiano, V Muthukumar, W Kong, E Brunskill International Conference on Artificial Intelligence and Statistics, 3340-3348, 2021 | 27 | 2021 |
| Robust meta-learning for mixed linear regression with small batches W Kong, R Somani, S Kakade, S Oh Advances in neural information processing systems 33, 4683-4696, 2020 | 22 | 2020 |
| Sublinear optimal policy value estimation in contextual bandits W Kong, E Brunskill, G Valiant International Conference on Artificial Intelligence and Statistics, 4377-4387, 2020 | 13 | 2020 |
| Recovering structured probability matrices Q Huang, SM Kakade, W Kong, G Valiant arXiv preprint arXiv:1602.06586, 2016 | 10 | 2016 |
| Optimal allocation for chunked-reward advertising W Kong, J Li, TY Liu, T Qin Web and Internet Economics: 9th International Conference, WINE 2013 …, 2013 | 7* | 2013 |
| Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics Z Miao, W Kong, RK Vinayak, W Sun, F Han Journal of the American Statistical Association, 1-13, 2022 | 4 | 2022 |
| Differential privacy and robust statistics in high dimensions X Liu, W Kong, S Oh Conference on Learning Theory, 1167-1246, 2022 | 4 | 2022 |
Gregory ValiantAssistant Professor of Computer Science, Stanford UniversityVerified email at stanford.edu
