| 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 | 333 | 2014 |
| 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 | 323 | 2016 |
| 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 | 201 | 2017 |
| Robust sensitivity analysis for stochastic systems H Lam Mathematics of Operations Research 41 (4), 1248-1275, 2016 | 131 | 2016 |
| Chernoff-Hoeffding bounds for Markov chains: Generalized and simplified KM Chung, H Lam, Z Liu, M Mitzenmacher arXiv preprint arXiv:1201.0559, 2012 | 113 | 2012 |
| Recovering best statistical guarantees via the empirical divergence-based distributionally robust optimization H Lam Operations Research 67 (4), 1090-1105, 2019 | 97 | 2019 |
| 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 | 75 | 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 | 71 | 2012 |
| Advanced tutorial: Input uncertainty and robust analysis in stochastic simulation H Lam 2016 Winter Simulation Conference (WSC), 178-192, 2016 | 64 | 2016 |
| The empirical likelihood approach to quantifying uncertainty in sample average approximation H Lam, E Zhou Operations Research Letters 45 (4), 301-307, 2017 | 54 | 2017 |
| Robust analysis in stochastic simulation: Computation and performance guarantees S Ghosh, H Lam Operations Research 67 (1), 232-249, 2019 | 45 | 2019 |
| Sensitivity to serial dependency of input processes: A robust approach H Lam Management Science 64 (3), 1311-1327, 2018 | 41 | 2018 |
| Learning-based robust optimization: Procedures and statistical guarantees LJ Hong, Z Huang, H Lam Management Science 67 (6), 3447-3467, 2021 | 37 | 2021 |
| 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 | 34 | 2017 |
| Tail analysis without parametric models: A worst-case perspective H Lam, C Mottet Operations Research 65 (6), 1696-1711, 2017 | 33 | 2017 |
| Information Dissemination via Random Walks in d-Dimensional Space H Lam, Z Liu, M Mitzenmacher, X Sun, Y Wang Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete …, 2012 | 27 | 2012 |
| Quantifying uncertainty in sample average approximation H Lam, E Zhou 2015 Winter Simulation Conference (WSC), 3846-3857, 2015 | 25 | 2015 |
| 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 | 24 | 2018 |
| Rare event simulation for a slotted time M/G/s model J Blanchet, P Glynn, H Lam Queueing Systems 63, 33-57, 2009 | 22 | 2009 |
| Accelerated evaluation of automated vehicles in lane change scenarios D Zhao, H Peng, H Lam, S Bao, K Nobukawa, DJ LeBlanc, CS Pan Dynamic Systems and Control Conference 57243, V001T17A002, 2015 | 21 | 2015 |
