| Network inference via the time-varying graphical lasso D Hallac, Y Park, S Boyd, J Leskovec Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 235 | 2017 |
| Domain adaptation for time series forecasting via attention sharing X Jin, Y Park, D Maddix, H Wang, Y Wang International Conference on Machine Learning, 10280-10297, 2022 | 57 | 2022 |
| Learning quantile functions without quantile crossing for distribution-free time series forecasting Y Park, D Maddix, FX Aubet, K Kan, J Gasthaus, Y Wang International Conference on Artificial Intelligence and Statistics, 8127-8150, 2022 | 31 | 2022 |
| Variable metric proximal gradient method with diagonal barzilai-borwein stepsize Y Park, S Dhar, S Boyd, M Shah ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 29 | 2020 |
| Multivariate quantile function forecaster K Kan, FX Aubet, T Januschowski, Y Park, K Benidis, L Ruthotto, ... International Conference on Artificial Intelligence and Statistics, 10603-10621, 2022 | 25 | 2022 |
| Learning the network structure of heterogeneous data via pairwise exponential Markov random fields Y Park, D Hallac, S Boyd, J Leskovec Artificial Intelligence and Statistics, 1302-1310, 2017 | 23 | 2017 |
| Variance reduced training with stratified sampling for forecasting models Y Lu, Y Park, L Chen, Y Wang, C De Sa, D Foster International Conference on Machine Learning, 7145-7155, 2021 | 22 | 2021 |
| First de-trend then attend: Rethinking attention for time-series forecasting X Zhang, X Jin, K Gopalswamy, G Gupta, Y Park, X Shi, H Wang, ... arXiv preprint arXiv:2212.08151, 2022 | 16 | 2022 |
| Structured policy iteration for linear quadratic regulator Y Park, R Rossi, Z Wen, G Wu, H Zhao International Conference on Machine Learning, 7521-7531, 2020 | 15 | 2020 |
| Robust probabilistic time series forecasting TH Yoon, Y Park, EK Ryu, Y Wang International Conference on Artificial Intelligence and Statistics, 1336-1358, 2022 | 12 | 2022 |
| Linear convergence of cyclic SAGA Y Park, EK Ryu Optimization Letters 14 (6), 1583-1598, 2020 | 12 | 2020 |
| Optimal operation of a plug-in hybrid vehicle with battery thermal and degradation model J Kim, Y Park, JD Fox, SP Boyd, W Dally 2020 American Control Conference (ACC), 3083-3090, 2020 | 12 | 2020 |
| Linear quadratic regulator for resource-efficient cloud services Y Park, K Mahadik, RA Rossi, G Wu, H Zhao Proceedings of the ACM Symposium on Cloud Computing, 488-489, 2019 | 9 | 2019 |
| But are you sure? an uncertainty-aware perspective on explainable ai C Marx, Y Park, H Hasson, Y Wang, S Ermon, L Huan International Conference on Artificial Intelligence and Statistics, 7375-7391, 2023 | 8 | 2023 |
| System and method for resource scaling for efficient resource management KV Mahadik, RA Rossi, SM Lee, G Theocharous, H Zhao, G Wu, Y Park US Patent 11,487,579, 2022 | 8 | 2022 |
| Robust multivariate time-series forecasting: adversarial attacks and defense mechanisms L Liu, Y Park, TN Hoang, H Hasson, J Huan arXiv preprint arXiv:2207.09572, 2022 | 4 | 2022 |
| Adaptive sampling for probabilistic forecasting under distribution shift L Masserano, SS Rangapuram, S Kapoor, RS Nirwan, Y Park, ... arXiv preprint arXiv:2302.11870, 2023 | 3 | 2023 |
| Towards robust multivariate time-series forecasting: adversarial attacks and defense mechanisms L Liu, Y Park, TN Hoang, H Hasson, J Huan KDD 2022 Workshop on Mining and Learning from Time Series-Deep Forecasting …, 2022 | 3 | 2022 |
| Trending now: Modeling trend recommendations H Ding, B Kveton, Y Ma, Y Park, V Kini, Y Gu, R Divvela, F Wang, ... Proceedings of the 17th ACM Conference on Recommender Systems, 294-305, 2023 | 1 | 2023 |
| Testing Causality for High Dimensional Data A Jambulapati, H Hasson, Y Park, Y Wang arXiv preprint arXiv:2303.07774, 2023 | 1 | 2023 |
Stephen BoydProfessor of Electrical Engineering, Computer Science, and Management Science, StanfordПотвърден имейл адрес: stanford.edu
