| Machine learning classification over encrypted data R Bost, RA Popa, S Tu, S Goldwasser Cryptology ePrint Archive, 2014 | 698 | 2014 |
| Processing analytical queries over encrypted data SL Tu, MF Kaashoek, SR Madden, N Zeldovich Association for Computing Machinery (ACM), 2013 | 438 | 2013 |
| Speedy transactions in multicore in-memory databases S Tu, W Zheng, E Kohler, B Liskov, S Madden Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems …, 2013 | 400 | 2013 |
| On the sample complexity of the linear quadratic regulator S Dean, H Mania, N Matni, B Recht, S Tu Foundations of Computational Mathematics 20 (4), 633-679, 2020 | 361 | 2020 |
| Low-rank solutions of linear matrix equations via procrustes flow S Tu, R Boczar, M Simchowitz, M Soltanolkotabi, B Recht arXiv preprint arXiv:1507.03566, 2015 | 337 | 2015 |
| Learning without mixing: Towards a sharp analysis of linear system identification M Simchowitz, H Mania, S Tu, MI Jordan, B Recht Conference On Learning Theory, 439-473, 2018 | 203 | 2018 |
| Regret bounds for robust adaptive control of the linear quadratic regulator S Dean, H Mania, N Matni, B Recht, S Tu Advances in Neural Information Processing Systems 31, 2018 | 185 | 2018 |
| Anti-caching: A new approach to database management system architecture J DeBrabant, A Pavlo, S Tu, M Stonebraker, S Zdonik Proceedings of the VLDB Endowment 6 (14), 1942-1953, 2013 | 141 | 2013 |
| Fast databases with fast durability and recovery through multicore parallelism W Zheng, S Tu, E Kohler, B Liskov 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2014 | 126 | 2014 |
| The gap between model-based and model-free methods on the linear quadratic regulator: An asymptotic viewpoint S Tu, B Recht Conference on Learning Theory, 3036-3083, 2019 | 105 | 2019 |
| Safely learning to control the constrained linear quadratic regulator S Dean, S Tu, N Matni, B Recht 2019 American Control Conference (ACC), 5582-5588, 2019 | 100 | 2019 |
| Least-squares temporal difference learning for the linear quadratic regulator S Tu, B Recht International Conference on Machine Learning, 5005-5014, 2018 | 91 | 2018 |
| Certainty equivalence is efficient for linear quadratic control H Mania, S Tu, B Recht Advances in Neural Information Processing Systems 32, 2019 | 85 | 2019 |
| Certainty equivalent control of lqr is efficient H Mania, S Tu, B Recht arXiv preprint arXiv:1902.07826, 2019 | 63 | 2019 |
| Cyclades: Conflict-free asynchronous machine learning X Pan, M Lam, S Tu, D Papailiopoulos, C Zhang, MI Jordan, ... Advances in Neural Information Processing Systems 29, 2016 | 62 | 2016 |
| The HipHop compiler for PHP H Zhao, I Proctor, M Yang, X Qi, M Williams, Q Gao, G Ottoni, A Paroski, ... ACM SIGPLAN Notices 47 (10), 575-586, 2012 | 58 | 2012 |
| From self-tuning regulators to reinforcement learning and back again N Matni, A Proutiere, A Rantzer, S Tu 2019 IEEE 58th Conference on Decision and Control (CDC), 3724-3740, 2019 | 57 | 2019 |
| Non-asymptotic analysis of robust control from coarse-grained identification S Tu, R Boczar, A Packard, B Recht arXiv preprint arXiv:1707.04791, 2017 | 57 | 2017 |
| Learning control barrier functions from expert demonstrations A Robey, H Hu, L Lindemann, H Zhang, DV Dimarogonas, S Tu, N Matni 2020 59th IEEE Conference on Decision and Control (CDC), 3717-3724, 2020 | 56 | 2020 |
| Observational overfitting in reinforcement learning X Song, Y Jiang, S Tu, Y Du, B Neyshabur arXiv preprint arXiv:1912.02975, 2019 | 49 | 2019 |
