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Annie S. Chen
Annie S. Chen
Stanford University
Email verificata su stanford.edu - Home page
Titolo
Citata da
Citata da
Anno
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
50122021
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
5502021
Surgical fine-tuning improves adaptation to distribution shifts
Y Lee*, AS Chen*, F Tajwar, A Kumar, H Yao, P Liang, C Finn
arXiv preprint arXiv:2210.11466, 2022
2092022
Learning generalizable robotic reward functions from" in-the-wild" human videos
AS Chen, S Nair, C Finn
Robotics: Science and Systems (RSS), 2021
131*2021
Language-driven representation learning for robotics
S Karamcheti, S Nair, AS Chen, T Kollar, C Finn, D Sadigh, P Liang
arXiv preprint arXiv:2302.12766, 2023
1252023
You Only Live Once: Single-Life Reinforcement Learning
AS Chen, A Sharma, S Levine, C Finn
Neural Information Processing Systems (NeurIPS), 2022
262022
Batch exploration with examples for scalable robotic reinforcement learning
AS Chen*, HJ Nam*, S Nair*, C Finn
IEEE Robotics and Automation Letters 6 (3), 4401-4408, 2021
242021
On the opportunities and risks of foundation models. arXiv [Preprint](2021)
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S Arx, ...
Source:〈 https://arxiv. org/abs/2108.07258, 0
21
On the opportunities and risks of foundation models. arXiv. 10.48550
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S Von Arx, ...
arXiv preprint arXiv.2108.07258, 2021
142021
On the opportunities and risks of foundation models. arXiv preprint
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S Arx, ...
arXiv preprint arXiv:2108.07258, 2021
132021
On the opportunities and risks of foundation models. CoRR abs/2108.07258 (2021)
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
Preprint at https://arxiv. org/abs/2108.07258 2108, 2021
132021
Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features
AS Chen, Y Lee, A Setlur, S Levine, C Finn
The Twelfth International Conference on Learning Representations, 0
13*
Index Divisibility in Dynamical Sequences and Cyclic Orbits Modulo
AS Chen, TA Gassert, KE Stange
New York Journal of Mathematics 23, 1045–1063, 2017
112017
On the Opportunities and Risks of Foundation Models, 2022. doi: 10.48550
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv.2108.07258 1, 0
10
Commonsense reasoning for legged robot adaptation with vision-language models
AS Chen, AM Lessing, A Tang, G Chada, L Smith, S Levine, C Finn
arXiv preprint arXiv:2407.02666, 2024
92024
Rlvf: Learning from verbal feedback without overgeneralization
M Stephan, A Khazatsky, E Mitchell, AS Chen, S Hsu, A Sharma, C Finn
arXiv preprint arXiv:2402.10893, 2024
72024
Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment
AS Chen, G Chada, L Smith, A Sharma, Z Fu, S Levine, C Finn
arXiv preprint arXiv:2311.01059, 2023
62023
On the opportunities and risks of foundation models [arXiv [cs. LG]]. 2021
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
5
Calibrating language models with adaptive temperature scaling
J Xie, AS Chen, Y Lee, E Mitchell, C Finn
arXiv preprint arXiv:2409.19817, 2024
32024
AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data
C Choi, Y Lee, A Chen, A Zhou, A Raghunathan, C Finn
arXiv preprint arXiv:2401.10220, 2024
32024
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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