| CloudRCA: A root cause analysis framework for cloud computing platforms Y Zhang, Z Guan, H Qian, L Xu, H Liu, Q Wen, L Sun, J Jiang, L Fan, M Ke Proceedings of the 30th ACM International Conference on Information …, 2021 | 61 | 2021 |
| Subsampling to enhance efficiency in input uncertainty quantification H Lam, H Qian Operations Research 70 (3), 1891-1913, 2022 | 35 | 2022 |
| RobustScaler: QoS-Aware Autoscaling for Complex Workloads H Qian, Q Wen, L Sun, J Gu, Q Niu, Z Tang 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2762-2775, 2022 | 27 | 2022 |
| Learning prediction intervals for regression: Generalization and calibration H Chen, Z Huang, H Lam, H Qian, H Zhang International Conference on Artificial Intelligence and Statistics, 820-828, 2021 | 26 | 2021 |
| The empirical likelihood approach to simulation input uncertainty H Lam, H Qian 2016 Winter Simulation Conference (WSC), 791-802, 2016 | 22 | 2016 |
| Discovery of significant pathways in breast cancer metastasis via module extraction and comparison X Wang, H Qian, S Zhang IET systems biology 8 (2), 47-55, 2014 | 16 | 2014 |
| Optimization-based quantification of simulation input uncertainty via empirical likelihood H Lam, H Qian arXiv preprint arXiv:1707.05917, 2017 | 15 | 2017 |
| AHPA: adaptive horizontal pod autoscaling systems on alibaba cloud container service for kubernetes Z Zhou, C Zhang, L Ma, J Gu, H Qian, Q Wen, L Sun, P Li, Z Tang Proceedings of the AAAI conference on artificial intelligence 37 (13), 15621 …, 2023 | 14 | 2023 |
| Optimization-based calibration of simulation input models A Goeva, H Lam, H Qian, B Zhang Operations research 67 (5), 1362-1382, 2019 | 14 | 2019 |
| Random perturbation and bagging to quantify input uncertainty H Lam, H Qian 2019 Winter Simulation Conference (WSC), 320-331, 2019 | 13 | 2019 |
| Bounding optimality gap in stochastic optimization via bagging: Statistical efficiency and stability H Lam, H Qian arXiv preprint arXiv:1810.02905, 2018 | 13 | 2018 |
| Subsampling variance for input uncertainty quantification H Lam, H Qian 2018 Winter simulation conference (WSC), 1611-1622, 2018 | 10 | 2018 |
| Combating conservativeness in data-driven optimization under uncertainty: A solution path approach H Lam, H Qian arXiv preprint arXiv:1909.06477, 2019 | 9 | 2019 |
| Assessing solution quality in stochastic optimization via bootstrap aggregating H Lam, H Qian 2018 Winter Simulation Conference (WSC), 2061-2071, 2018 | 9 | 2018 |
| Reconstructing input models in stochastic simulation A Goeva, H Lam, H Qian, B Zhang Proceedings of the 2014 Winter Simulation Conference, IEEE Press, 698-709, 2014 | 2 | 2014 |
| Subsampled Ensemble Can Improve Generalization Tail Exponentially H Qian, D Ying, H Lam, W Yin arXiv preprint arXiv:2405.14741, 2024 | | 2024 |
| HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent Z Chen, J Lu, H Qian, X Wang, W Yin International Conference on Artificial Intelligence and Statistics, 10732-10781, 2023 | | 2023 |
| Uncertainty Quantification in Data-Driven Simulation and Optimization: Statistical and Computational Efficiency H Qian Columbia University, 2020 | | 2020 |
| Validating optimization with uncertain constraints H Lam, H Qian 2019 Winter Simulation Conference (WSC), 3621-3632, 2019 | | 2019 |
| Unveiling Causal Relationships Among Candidate Output Tokens in Large Language Models: Towards Interpretability and Control HP Wang, X Chen, H Qian, W Yin, X Wang | | |
