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Richard Liaw
Richard Liaw
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Ray: A distributed framework for emerging {AI} applications
P Moritz, R Nishihara, S Wang, A Tumanov, R Liaw, E Liang, M Elibol, ...
13th {USENIX} Symposium on Operating Systems Design and Implementation …, 2018
8202018
RLlib: Abstractions for distributed reinforcement learning
E Liang, R Liaw, R Nishihara, P Moritz, R Fox, K Goldberg, J Gonzalez, ...
International Conference on Machine Learning, 3053-3062, 2018
664*2018
Tune: A research platform for distributed model selection and training
R Liaw, E Liang, R Nishihara, P Moritz, JE Gonzalez, I Stoica
arXiv preprint arXiv:1807.05118, 2018
4912018
Benchmarks for reinforcement learning in mixed-autonomy traffic
E Vinitsky, A Kreidieh, L Le Flem, N Kheterpal, K Jang, C Wu, F Wu, ...
Conference on robot learning, 399-409, 2018
1142018
Real-time machine learning: The missing pieces
R Nishihara, P Moritz, S Wang, A Tumanov, W Paul, J Schleier-Smith, ...
Proceedings of the 16th Workshop on Hot Topics in Operating Systems, 106-110, 2017
622017
SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards
S Krishnan, A Garg, R Liaw, B Thananjeyan, L Miller, FT Pokorny, ...
The international journal of robotics research 38 (2-3), 126-145, 2019
462019
Large batch size training of neural networks with adversarial training and second-order information
Z Yao, A Gholami, D Arfeen, R Liaw, J Gonzalez, K Keutzer, M Mahoney
arXiv preprint arXiv:1810.01021, 2018
402018
Hirl: Hierarchical inverse reinforcement learning for long-horizon tasks with delayed rewards
S Krishnan, A Garg, R Liaw, L Miller, FT Pokorny, K Goldberg
arXiv preprint arXiv:1604.06508, 2016
342016
Hypersched: Dynamic resource reallocation for model development on a deadline
R Liaw, R Bhardwaj, L Dunlap, Y Zou, JE Gonzalez, I Stoica, A Tumanov
Proceedings of the ACM Symposium on Cloud Computing, 61-73, 2019
332019
Iterative noise injection for scalable imitation learning
M Laskey, J Lee, W Hsieh, R Liaw, J Mahler, R Fox, K Goldberg
arXiv preprint arXiv:1703.09327, 2017
222017
Composing meta-policies for autonomous driving using hierarchical deep reinforcement learning
R Liaw, S Krishnan, A Garg, D Crankshaw, JE Gonzalez, K Goldberg
arXiv preprint arXiv:1711.01503, 2017
182017
Ray: a distributed framework for emerging AI applications. CoRR abs/1712.05889 (2017)
P Moritz, R Nishihara, S Wang, A Tumanov, R Liaw, E Liang, W Paul, ...
arXiv preprint arXiv:1712.05889, 2017
182017
RubberBand: cloud-based hyperparameter tuning
U Misra, R Liaw, L Dunlap, R Bhardwaj, K Kandasamy, JE Gonzalez, ...
Proceedings of the Sixteenth European Conference on Computer Systems, 327-342, 2021
162021
SWIRL: A SequentialWindowed Inverse Reinforcement Learning Algorithm for Robot Tasks With Delayed Rewards
S Krishnan, A Garg, R Liaw, B Thananjeyan, L Miller, FT Pokorny, ...
Algorithmic Foundations of Robotics XII: Proceedings of the Twelfth Workshop …, 2020
132020
Impact: Importance weighted asynchronous architectures with clipped target networks
M Luo, J Yao, R Liaw, E Liang, I Stoica
arXiv preprint arXiv:1912.00167, 2019
52019
HIRL: Hierarchical Inverse Reinforcement Learning for Long-Horizon Tasks with Delayed Rewards. CoRR abs/1604.06508 (2016)
S Krishnan, A Garg, R Liaw, L Miller, FT Pokorny, K Goldberg
32016
Elastic Hyperparameter Tuning on the Cloud
L Dunlap, K Kandasamy, U Misra, R Liaw, M Jordan, I Stoica, ...
Proceedings of the ACM Symposium on Cloud Computing, 33-46, 2021
12021
ESCHER: expressive scheduling with ephemeral resources
R Bhardwaj, A Tumanov, S Wang, R Liaw, P Moritz, R Nishihara, I Stoica
Proceedings of the 13th Symposium on Cloud Computing, 47-62, 2022
2022
REVEAL 2022: Reinforcement Learning-Based Recommender Systems at Scale
R Liaw, P Bailey, Y Li, M Dimakopoulou, Y Raimond
Proceedings of the 16th ACM Conference on Recommender Systems, 684-685, 2022
2022
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