| Machine learning classification over encrypted data R Bost, RA Popa, S Tu, S Goldwasser Cryptology ePrint Archive, 2014 | 995 | 2014 |
| 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 | 636 | 2020 |
| 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 | 564 | 2013 |
| Processing analytical queries over encrypted data SL Tu, MF Kaashoek, SR Madden, N Zeldovich Association for Computing Machinery (ACM), 2013 | 505 | 2013 |
| 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 | 441 | 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 | 387 | 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 | 310 | 2018 |
| Certainty equivalence is efficient for linear quadratic control H Mania, S Tu, B Recht Advances in Neural Information Processing Systems 32, 2019 | 234 | 2019 |
| 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 | 231 | 2020 |
| 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 | 182 | 2019 |
| 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 | 181 | 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 | 173 | 2014 |
| Robots that ask for help: Uncertainty alignment for large language model planners AZ Ren, A Dixit, A Bodrova, S Singh, S Tu, N Brown, P Xu, L Takayama, ... arXiv preprint arXiv:2307.01928, 2023 | 168 | 2023 |
| 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 | 165 | 2019 |
| Observational overfitting in reinforcement learning X Song, Y Jiang, S Tu, Y Du, B Neyshabur arXiv preprint arXiv:1912.02975, 2019 | 148 | 2019 |
| Least-squares temporal difference learning for the linear quadratic regulator S Tu, B Recht International Conference on Machine Learning, 5005-5014, 2018 | 144 | 2018 |
| 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 | 105 | 2019 |
| Learning stability certificates from data N Boffi, S Tu, N Matni, JJ Slotine, V Sindhwani Conference on Robot Learning, 1341-1350, 2021 | 96 | 2021 |
| Certainty equivalent control of LQR is efficient H Mania, S Tu, B Recht arXiv preprint arXiv:1902.07826, 2019 | 80 | 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 | 79 | 2017 |
