| Accelerated evaluation of automated vehicles safety in lane-change scenarios based on importance sampling techniques D Zhao, H Lam, H Peng, S Bao, DJ LeBlanc, K Nobukawa, CS Pan IEEE transactions on intelligent transportation systems 18 (3), 595-607, 2016 | 442 | 2016 |
| Learning about social learning in MOOCs: From statistical analysis to generative model CG Brinton, M Chiang, S Jain, H Lam, Z Liu, FMF Wong IEEE transactions on Learning Technologies 7 (4), 346-359, 2014 | 366 | 2014 |
| Accelerated evaluation of automated vehicles in car-following maneuvers D Zhao, X Huang, H Peng, H Lam, DJ LeBlanc IEEE Transactions on Intelligent Transportation Systems 19 (3), 733-744, 2017 | 250 | 2017 |
| Robust sensitivity analysis for stochastic systems H Lam Mathematics of Operations Research 41 (4), 1248-1275, 2016 | 174 | 2016 |
| Recovering best statistical guarantees via the empirical divergence-based distributionally robust optimization H Lam Operations Research 67 (4), 1090-1105, 2019 | 141 | 2019 |
| Chernoff-Hoeffding bounds for Markov chains: Generalized and simplified KM Chung, H Lam, Z Liu, M Mitzenmacher arXiv preprint arXiv:1201.0559, 2012 | 124 | 2012 |
| Accelerated evaluation of automated vehicles using piecewise mixture models Z Huang, H Lam, DJ LeBlanc, D Zhao IEEE Transactions on Intelligent Transportation Systems 19 (9), 2845-2855, 2017 | 96 | 2017 |
| State-dependent importance sampling for rare-event simulation: An overview and recent advances J Blanchet, H Lam Surveys in Operations Research and Management Science 17 (1), 38-59, 2012 | 88 | 2012 |
| Advanced tutorial: Input uncertainty and robust analysis in stochastic simulation H Lam 2016 Winter Simulation Conference (WSC), 178-192, 2016 | 85 | 2016 |
| The empirical likelihood approach to quantifying uncertainty in sample average approximation H Lam, E Zhou Operations Research Letters 45 (4), 301-307, 2017 | 68 | 2017 |
| Learning-based robust optimization: Procedures and statistical guarantees LJ Hong, Z Huang, H Lam Management Science 67 (6), 3447-3467, 2021 | 60 | 2021 |
| Robust analysis in stochastic simulation: Computation and performance guarantees S Ghosh, H Lam Operations Research 67 (1), 232-249, 2019 | 56 | 2019 |
| Sensitivity to serial dependency of input processes: A robust approach H Lam Management Science 64 (3), 1311-1327, 2018 | 55 | 2018 |
| Tail analysis without parametric models: A worst-case perspective H Lam, C Mottet Operations Research 65 (6), 1696-1711, 2017 | 51 | 2017 |
| Evaluation of automated vehicles in the frontal cut-in scenario—An enhanced approach using piecewise mixture models Z Huang, D Zhao, H Lam, DJ LeBlanc, H Peng 2017 IEEE International Conference on Robotics and Automation (ICRA), 197-202, 2017 | 42 | 2017 |
| A versatile approach to evaluating and testing automated vehicles based on kernel methods Z Huang, Y Guo, M Arief, H Lam, D Zhao 2018 Annual American Control Conference (ACC), 4796-4802, 2018 | 36 | 2018 |
| Deep probabilistic accelerated evaluation: A robust certifiable rare-event simulation methodology for black-box safety-critical systems M Arief, Z Huang, GKS Kumar, Y Bai, S He, W Ding, H Lam, D Zhao International Conference on Artificial Intelligence and Statistics, 595-603, 2021 | 35 | 2021 |
| Subsampling to enhance efficiency in input uncertainty quantification H Lam, H Qian Operations Research 70 (3), 1891-1913, 2022 | 33 | 2022 |
| Towards affordable on-track testing for autonomous vehicle—A Kriging-based statistical approach Z Huang, H Lam, D Zhao 2017 ieee 20th international conference on intelligent transportation …, 2017 | 30 | 2017 |
| Quantifying uncertainty in sample average approximation H Lam, E Zhou 2015 Winter Simulation Conference (WSC), 3846-3857, 2015 | 30 | 2015 |
