| Asymptotics for sketching in least squares regression E Dobriban, S Liu Advances in Neural Information Processing Systems 32, 3675-3685, 2019 | 74* | 2019 |
| Ridge regression: Structure, cross-validation, and sketching S Liu, E Dobriban Eighth International Conference on Learning Representations (ICLR 2020), 2019 | 70 | 2019 |
| Global and individualized community detection in inhomogeneous multilayer networks S Chen, S Liu, Z Ma The Annals of Statistics 50 (5), 2664-2693, 2022 | 46 | 2022 |
| How to reduce dimension with PCA and random projections? F Yang, S Liu, E Dobriban, DP Woodruff IEEE Transactions on Information Theory 67 (12), 8154-8189, 2021 | 42 | 2021 |
| Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform J Lacotte, S Liu, E Dobriban, M Pilanci Advances in Neural Information Processing Systems 33, 2020 | 30* | 2020 |
| Quasi-Monte Carlo Quasi-Newton in Variational Bayes S Liu, AB Owen Journal of Machine Learning Research 22 (243), 1-23, 2021 | 25* | 2021 |
| Preintegration via Active Subspace S Liu, AB Owen SIAM Journal on Numerical Analysis 61 (2), 495-514, 2023 | 13 | 2023 |
| Statistical challenges in tracking the evolution of SARS-CoV-2 L Cappello, J Kim, S Liu, JA Palacios Statistical Science 37 (2), 162-182, 2022 | 9 | 2022 |
| Langevin Quasi-Monte Carlo S Liu Advances in Neural Information Processing Systems 36, 75338-75353, 2023 | 5 | 2023 |
| Selective inference with distributed data S Liu, S Panigrahi Journal of Machine Learning Research 26 (12), 1-44, 2025 | 4 | 2025 |
| An Exact Sampler for Inference after Polyhedral Model Selection S Liu arXiv preprint arXiv:2308.10346, 2023 | 4 | 2023 |
| Black-box selective inference via bootstrapping S Liu, J Markovic-Voronov, J Taylor arXiv preprint arXiv:2203.14504, 2022 | 4 | 2022 |
| Conditional quasi-Monte Carlo with constrained active subspaces S Liu SIAM Journal on Scientific Computing 46 (5), A2999-A3021, 2024 | 3 | 2024 |
| Transport Quasi-Monte Carlo S Liu arXiv preprint arXiv:2412.16416, 2024 | 2 | 2024 |
| The No-Underrun Sampler: A Locally-Adaptive, Gradient-Free MCMC Method N Bou-Rabee, B Carpenter, S Liu, S Oberdörster arXiv preprint arXiv:2501.18548, 2025 | | 2025 |
| Cross-Validation with Antithetic Gaussian Randomization S Liu, S Panigrahi, JA Soloff arXiv preprint arXiv:2412.14423, 2024 | | 2024 |
Edgar DobribanStatistics & Computer Science, University of PennsylvaniaVerified email at upenn.edu
