Artykuły udostępnione publicznie: - Chelsea FinnWięcej informacji
Dostępne w jakimś miejscu: 83
Model-agnostic meta-learning for fast adaptation of deep networks
C Finn, P Abbeel, S Levine
International Conference on Machine Learning (ICML), 1126-1135, 2017
Upoważnienia: US National Science Foundation, US Department of Defense
End-to-end training of deep visuomotor policies
S Levine, C Finn, T Darrell, P Abbeel
Journal of Machine Learning Research 17 (1), 1334-1373, 2016
Upoważnienia: US National Science Foundation, US National Institutes of Health, Natural …
Direct preference optimization: Your language model is secretly a reward model
R Rafailov, A Sharma, E Mitchell, S Ermon, CD Manning, C Finn
Neural Information Processing Systems (NeurIPS), 2023
Upoważnienia: US Department of Defense
Wilds: A benchmark of in-the-wild distribution shifts
PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ...
International Conference on Machine Learning (ICML), 5637-5664, 2021
Upoważnienia: US National Science Foundation, US Department of Defense, Marcus and Amalia …
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T Yu, D Quillen, Z He, R Julian, K Hausman, C Finn, S Levine
Conference on Robot Learning (CoRL), 2019
Upoważnienia: US National Science Foundation, US Department of Defense
Guided cost learning: Deep inverse optimal control via policy optimization
C Finn, S Levine, P Abbeel
International Conference on Machine Learning (ICML), 49-58, 2016
Upoważnienia: US National Science Foundation
Gradient surgery for multi-task learning
T Yu, S Kumar, A Gupta, S Levine, K Hausman, C Finn
Neural Information Processing Systems (NeurIPS), 2020
Upoważnienia: US National Science Foundation
Meta-learning with implicit gradients
A Rajeswaran, C Finn, S Kakade, S Levine
Neural Information Processing Systems (NeurIPS), 2019
Upoważnienia: US National Science Foundation, US Department of Defense
Probabilistic model-agnostic meta-learning
C Finn, K Xu, S Levine
Neural Information Processing Systems (NeurIPS), 2018
Upoważnienia: US National Science Foundation, US Department of Defense
Efficient off-policy meta-reinforcement learning via probabilistic context variables
K Rakelly, A Zhou, D Quillen, C Finn, S Levine
International Conference on Machine Learning (ICML), 2019
Upoważnienia: US National Science Foundation, US Department of Defense
One-shot visual imitation learning via meta-learning
C Finn, T Yu, T Zhang, P Abbeel, S Levine
Conference on Robot Learning (CoRL), 2017
Upoważnienia: US National Science Foundation, US Department of Defense
Just train twice: Improving group robustness without training group information
EZ Liu, B Haghgoo, AS Chen, A Raghunathan, PW Koh, S Sagawa, ...
International Conference on Machine Learning, 6781-6792, 2021
Upoważnienia: US National Science Foundation
Online meta-learning
C Finn, A Rajeswaran, S Kakade, S Levine
International Conference on Machine Learning (ICML), 2019
Upoważnienia: US National Science Foundation
Stochastic adversarial video prediction
AX Lee, R Zhang, F Ebert, P Abbeel, C Finn, S Levine
arXiv preprint arXiv:1804.01523, 2018
Upoważnienia: US National Science Foundation, US Department of Defense
Combo: Conservative offline model-based policy optimization
T Yu, A Kumar, R Rafailov, A Rajeswaran, S Levine, C Finn
Neural Information Processing Systems (NeurIPS), 2021
Upoważnienia: US Department of Defense
Self-supervised visual planning with temporal skip connections
F Ebert, C Finn, AX Lee, S Levine
Conference on Robot Learning (CoRL), 2017
Upoważnienia: US National Science Foundation
Adaptive risk minimization: Learning to adapt to domain shift
M Zhang, H Marklund, N Dhawan, A Gupta, S Levine, C Finn
Advances in Neural Information Processing Systems 34, 23664-23678, 2021
Upoważnienia: US National Science Foundation, US Department of Defense, Marcus and Amalia …
Memo: Test time robustness via adaptation and augmentation
M Zhang, S Levine, C Finn
Advances in neural information processing systems 35, 38629-38642, 2022
Upoważnienia: US Department of Defense
Recovery rl: Safe reinforcement learning with learned recovery zones
B Thananjeyan, A Balakrishna, S Nair, M Luo, K Srinivasan, M Hwang, ...
IEEE Robotics and Automation Letters 6 (3), 4915-4922, 2021
Upoważnienia: US National Science Foundation
Entity abstraction in visual model-based reinforcement learning
R Veerapaneni, JD Co-Reyes, M Chang, M Janner, C Finn, J Wu, ...
Conference on Robot Learning, 1439-1456, 2019
Upoważnienia: US National Science Foundation, US Department of Defense
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