| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 3218 | 2021 |
| Unsupervised learning via meta-learning K Hsu, S Levine, C Finn International Conference on Learning Representations (ICLR), 2019 | 274 | 2019 |
| On the role of data in PAC-Bayes bounds GK Dziugaite, K Hsu, W Gharbieh, G Arpino, DM Roy International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 | 100 | 2021 |
| Unsupervised curricula for visual meta-reinforcement learning A Jabri, K Hsu, B Eysenbach, A Gupta, S Levine, C Finn Neural Information Processing Systems (NeurIPS), 2019 | 76 | 2019 |
| Multi-layered abstraction-based controller synthesis for continuous-time systems K Hsu, R Majumdar, K Mallik, AK Schmuck International Conference on Hybrid Systems: Computation and Control (HSCC), 2018 | 69 | 2018 |
| Germanium wrap-around photodetectors on silicon photonics R Going, TJ Seok, J Loo, K Hsu, MC Wu Optics Express, 2015 | 51 | 2015 |
| Differentiable annealed importance sampling and the perils of gradient noise G Zhang, K Hsu, J Li, C Finn, R Grosse Neural Information Processing Systems (NeurIPS), 2021 | 31 | 2021 |
| Vision-based manipulators need to also see from their hands K Hsu*, MJ Kim*, R Rafailov, J Wu, C Finn International Conference on Learning Representations (ICLR), 2022 | 28 | 2022 |
| Lazy abstraction-based control for safety specifications K Hsu, R Majumdar, K Mallik, AK Schmuck Conference on Decision and Control (CDC), 2018 | 22 | 2018 |
| Lazy abstraction-based controller synthesis K Hsu, R Majumdar, K Mallik, AK Schmuck International Symposium on Automated Technology for Verification and …, 2019 | 12* | 2019 |
| DROID: a large-scale in-the-wild robot manipulation dataset A Khazatsky, K Pertsch, S Nair, A Balakrishna, S Dasari, S Karamcheti, ... Robotics: Science and Systems (RSS), 2024 | 8 | 2024 |
| Disentanglement via latent quantization K Hsu, W Dorrell, JCR Whittington, J Wu, C Finn Neural Information Processing Systems (NeurIPS), 2023 | 8 | 2023 |
| Tripod: three complementary inductive biases for disentangled representation learning K Hsu*, JI Hamid*, K Burns, C Finn, J Wu International Conference on Machine Learning (ICML), 2024 | 1 | 2024 |
| What makes certain pre-trained visual representations better for robotic learning? K Hsu, TGW Lum, R Gao, SS Gu, J Wu, C Finn NeurIPS 2022 Foundation Models for Decision Making Workshop, 2022 | 1 | 2022 |
| Evaluating real-world robot manipulation policies in simulation X Li*, K Hsu*, J Gu*, K Pertsch, O Mees, HR Walke, C Fu, I Lunawat, ... arXiv preprint arXiv:2405.05941, 2024 | | 2024 |
Chelsea FinnStanford University, Google在 cs.stanford.edu 的电子邮件经过验证
