| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 6992 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 3439 | 2024 |
| Decision transformer: Reinforcement learning via sequence modeling L Chen, K Lu, A Rajeswaran, K Lee, A Grover, M Laskin, P Abbeel, ... Advances in neural information processing systems 34, 15084-15097, 2021 | 2621 | 2021 |
| Curl: Contrastive unsupervised representations for reinforcement learning M Laskin, A Srinivas, P Abbeel International conference on machine learning, 5639-5650, 2020 | 1479 | 2020 |
| Reinforcement learning with augmented data M Laskin, K Lee, A Stooke, L Pinto, P Abbeel, A Srinivas Advances in neural information processing systems 33, 19884-19895, 2020 | 958 | 2020 |
| Decoupling representation learning from reinforcement learning A Stooke, K Lee, P Abbeel, M Laskin International conference on machine learning, 9870-9879, 2021 | 455 | 2021 |
| Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning K Lee, M Laskin, A Srinivas, P Abbeel International conference on machine learning, 6131-6141, 2021 | 325 | 2021 |
| Urlb: Unsupervised reinforcement learning benchmark M Laskin, D Yarats, H Liu, K Lee, A Zhan, K Lu, C Cang, L Pinto, P Abbeel arXiv preprint arXiv:2110.15191, 2021 | 217 | 2021 |
| In-context reinforcement learning with algorithm distillation M Laskin, L Wang, J Oh, E Parisotto, S Spencer, R Steigerwald, ... arXiv preprint arXiv:2210.14215, 2022 | 214 | 2022 |
| Fractional quantum Hall effect in a curved space: gravitational anomaly and electromagnetic response T Can, M Laskin, P Wiegmann Physical review letters 113 (4), 046803, 2014 | 167 | 2014 |
| Don't change the algorithm, change the data: Exploratory data for offline reinforcement learning D Yarats, D Brandfonbrener, H Liu, M Laskin, P Abbeel, A Lazaric, L Pinto arXiv preprint arXiv:2201.13425, 2022 | 141 | 2022 |
| Geometry of quantum Hall states: Gravitational anomaly and transport coefficients T Can, M Laskin, PB Wiegmann Annals of Physics 362, 752-794, 2015 | 114 | 2015 |
| A framework for efficient robotic manipulation A Zhan, R Zhao, L Pinto, P Abbeel, M Laskin Deep RL Workshop NeurIPS 2021, 2021 | 110 | 2021 |
| Cic: Contrastive intrinsic control for unsupervised skill discovery M Laskin, H Liu, XB Peng, D Yarats, A Rajeswaran, P Abbeel arXiv preprint arXiv:2202.00161, 2022 | 85 | 2022 |
| Sparse graphical memory for robust planning S Emmons, A Jain, M Laskin, T Kurutach, P Abbeel, D Pathak Advances in neural information processing systems 33, 5251-5262, 2020 | 66 | 2020 |
| Hierarchical few-shot imitation with skill transition models K Hakhamaneshi, R Zhao, A Zhan, P Abbeel, M Laskin arXiv preprint arXiv:2107.08981, 2021 | 63 | 2021 |
| Emergent conformal symmetry and geometric transport properties of quantum Hall states on singular surfaces T Can, YH Chiu, M Laskin, P Wiegmann Physical review letters 117 (26), 266803, 2016 | 62 | 2016 |
| Vision-language models as a source of rewards K Baumli, S Baveja, F Behbahani, H Chan, G Comanici, S Flennerhag, ... arXiv preprint arXiv:2312.09187, 2023 | 59 | 2023 |
| Unsupervised reinforcement learning with contrastive intrinsic control M Laskin, H Liu, XB Peng, D Yarats, A Rajeswaran, P Abbeel Advances in Neural Information Processing Systems 35, 34478-34491, 2022 | 59 | 2022 |
| Skill preferences: Learning to extract and execute robotic skills from human feedback X Wang, K Lee, K Hakhamaneshi, P Abbeel, M Laskin Conference on robot learning, 1259-1268, 2022 | 59 | 2022 |
