| Long-term forecasting with tide: Time-series dense encoder A Das, W Kong, A Leach, S Mathur, R Sen, R Yu arXiv preprint arXiv:2304.08424, 2023 | 718 | 2023 |
| A decoder-only foundation model for time-series forecasting A Das, W Kong, R Sen, Y Zhou Forty-first International Conference on Machine Learning, 2024 | 664 | 2024 |
| Isotropic hashing W Kong, WJ Li Advances in neural information processing systems 25, 2012 | 387 | 2012 |
| Spectre: Defending against backdoor attacks using robust covariance estimation J Hayase, W Kong International Conference on Machine Learning, 2020 | 256* | 2020 |
| 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 | 196 | 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 | 121 | 2012 |
| Robust and differentially private mean estimation X Liu, W Kong, S Kakade, S Oh Advances in neural information processing systems 34, 3887-3901, 2021 | 112 | 2021 |
| Double-bit quantization for hashing W Kong, WJ Li Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 634-640, 2012 | 110 | 2012 |
| Differential privacy and robust statistics in high dimensions X Liu, W Kong, S Oh Conference on Learning Theory, 1167-1246, 2022 | 108 | 2022 |
| Spectrum estimation from samples W Kong, G Valiant | 100 | 2017 |
| Meta-learning for mixed linear regression W Kong, R Somani, Z Song, S Kakade, S Oh International Conference on Machine Learning, 5394-5404, 2020 | 90 | 2020 |
| 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 | 70 | 2018 |
| Maximum likelihood estimation for learning populations of parameters RK Vinayak, W Kong, G Valiant, S Kakade International Conference on Machine Learning, 6448-6457, 2019 | 61 | 2019 |
| 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 | 58 | 2012 |
| Learning populations of parameters K Tian, W Kong, G Valiant Advances in neural information processing systems 30, 2017 | 52 | 2017 |
| 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 | 49 | 2021 |
| Estimating learnability in the sublinear data regime W Kong, G Valiant Advances in Neural Information Processing Systems 31, 2018 | 47 | 2018 |
| 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 | 45 | 2020 |
| DP-PCA: Statistically optimal and differentially private PCA X Liu, W Kong, P Jain, S Oh Advances in neural information processing systems 35, 29929-29943, 2022 | 38 | 2022 |
| Label robust and differentially private linear regression: Computational and statistical efficiency X Liu, P Jain, W Kong, S Oh, A Suggala Advances in Neural Information Processing Systems 36, 23019-23033, 2023 | 21* | 2023 |
sewoong ohProfessor, University of WashingtonVerified email at cs.washington.edu
