| 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 | 4909 | 2021 |
| Open X-Embodiment: robotic learning datasets and RT-X models OXE Collaboration International Conference on Robotics and Automation (ICRA), 2024 | 507* | 2024 |
| Unsupervised learning via meta-learning K Hsu, S Levine, C Finn International Conference on Learning Representations (ICLR), 2019 | 300 | 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 | 136 | 2024 |
| 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 | 117 | 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 | 79 | 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 | 78 | 2018 |
| Germanium wrap-around photodetectors on silicon photonics R Going, TJ Seok, J Loo, K Hsu, MC Wu Optics Express, 2015 | 52 | 2015 |
| Evaluating real-world robot manipulation policies in simulation X Li*, K Hsu*, J Gu*, K Pertsch, O Mees, HR Walke, C Fu, I Lunawat, ... Conference on Robot Learning (CoRL), 2024 | 45 | 2024 |
| 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 | 43 | 2022 |
| 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 | 38 | 2021 |
| Lazy abstraction-based control for safety specifications K Hsu, R Majumdar, K Mallik, AK Schmuck Conference on Decision and Control (CDC), 2018 | 25 | 2018 |
| Disentanglement via latent quantization K Hsu, W Dorrell, JCR Whittington, J Wu, C Finn Neural Information Processing Systems (NeurIPS), 2023 | 21 | 2023 |
| Lazy abstraction-based controller synthesis K Hsu, R Majumdar, K Mallik, AK Schmuck International Symposium on Automated Technology for Verification and …, 2019 | 14* | 2019 |
| 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 | 5 | 2024 |
| FSPO: few-shot preference optimization of synthetic preference data in LLMs elicits effective personalization to real users A Singh, S Hsu, K Hsu, E Mitchell, S Ermon, T Hashimoto, A Sharma, ... arXiv preprint arXiv:2502.19312, 2025 | | 2025 |
| Range, not independence, drives modularity in biologically inspired representations W Dorrell*, K Hsu*, L Hollingsworth, JH Lee, J Wu, C Finn, PE Latham, ... International Conference on Learning Representations (ICLR), 2025 | | 2025 |
Chelsea FinnStanford University, Physical IntelligenceVerified email at cs.stanford.edu