| Angle-based joint and individual variation explained Q Feng, M Jiang, J Hannig, JS Marron Journal of multivariate analysis 166, 241-265, 2018 | 100 | 2018 |
| A note on automatic data transformation Q Feng, J Hannig, JS Marron Stat 5 (1), 82-87, 2016 | 25 | 2016 |
| High-dimensional contextual policy search with unknown context rewards using Bayesian optimization Q Feng, B Letham, H Mao, E Bakshy Advances in Neural Information Processing Systems 33, 22032-22044, 2020 | 15 | 2020 |
| The user cost of low-income homeownership SF Riley, HY Ru, Q Feng Journal of Regional Analysis and Policy 43 (2), 123-137, 2013 | 14 | 2013 |
| Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization W Maddox, Q Feng, M Balandat Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021 …, 2021 | 10 | 2021 |
| Sparse bayesian optimization S Liu, Q Feng, D Eriksson, B Letham, E Bakshy International Conference on Artificial Intelligence and Statistics, 3754-3774, 2023 | 9 | 2023 |
| Non-iterative joint and individual variation explained Q Feng, J Hannig, JS Marron arXiv preprint arXiv:1512.04060, 2015 | 9 | 2015 |
| Fusion learning for inter-laboratory comparisons J Hannig, Q Feng, H Iyer, CM Wang, X Liu Journal of Statistical Planning and Inference 195, 64-79, 2018 | 8 | 2018 |
| One-Shot Optimal Design for Gaussian Process Analysis of Randomized Experiments J Markovic-Voronov, Q Feng, E Bakshy Sixth Workshop on Meta-Learning at the Conference on Neural Information …, 2022 | | 2022 |
| Gleaning Insights from Uber’s Partner Activity Matrix with Genomic Biclustering and Machine Learning Q Feng, P Frazier https://eng.uber.com/activity-matrix/, 2017 | | 2017 |
| Statistical integration of information Q Feng The University of North Carolina at Chapel Hill, 2016 | | 2016 |
Jan HannigProfessor of Statistics, University of North CarolinaVerified email at unc.edu
